Enterprise computing just got a major upgrade. With the launch of IBM z17™, organizations running mission-critical workloads now have access to a platform purpose-built for AI at scale, end-to-end automation, and advanced security capabilities. For the enterprises that run the world’s most important transactions in banking, insurance, retail, and government, this is a significant step forward.

A more powerful platform also means more complexity to manage. More AI workloads in production, more transactions per second, and more pressure on the teams responsible for keeping it all running.

That’s where Dynatrace comes in. As a trusted IBM partner, Dynatrace brings AI-powered, full-stack observability to the mainframe, giving teams the visibility and intelligence they need to operate IBM z17 environments with confidence.

IBM z17™: A new era for enterprise AI and automation

IBM z17 is designed for the next era of enterprise computing, bringing AI, automation, and security closer to the mission-critical transactions that run the business.

With the IBM Telum II™ processor and IBM Spyre™ Accelerator, organizations can run AI inferencing directly within transaction flows, helping support real-time decisions without moving data off-platform.

But as AI-powered workloads become more embedded in core systems, teams need more than infrastructure performance. They need real-time visibility into how those workloads behave across the full hybrid environment.

The observability challenge on the mainframe

As organizations accelerate AI adoption and automation on IBM z17, how do teams know it’s all working?

Mission-critical mainframe workloads have always been difficult to observe. Legacy monitoring tools were built for a different era, designed for periodic sampling rather than real-time intelligence. They tell you something went wrong after the fact, not why, and not how to fix it.

Bringing AI inference into live transaction flows raises the stakes. Unexpected model behavior, workload spikes, or latency issues can affect critical services, so teams need to understand what is happening and respond in seconds, not hours.

Dynatrace mainframe monitoring is built specifically to close that gap.

Dynatrace mainframe monitoring: Full-stack visibility for the core

Dynatrace mainframe monitoring gives enterprises deep, real-time visibility into their IBM Z environments, unified with the rest of their technology stack in a single platform, helping to reduce silos, minimize manual correlation, and support more informed decision making.

Here is what that means in practice:

• Real-time performance monitoring. Dynatrace continuously monitors CPU utilization, response times, transaction throughput, and resource consumption on IBM Z, surfacing anomalies when they occur rather than after a batch job completes.
• AI-powered root cause analysis. Dynatrace Intelligence, an agentic operations system, can analyze billions of dependencies across mainframe, cloud, and hybrid environments. When something goes wrong, Dynatrace Intelligence pinpoints the root cause, reducing mean time to resolution and freeing teams from manual investigation.
• End-to-end transaction tracing. Dynatrace traces transactions across the full hybrid stack, from the web front end through microservices, APIs, and into the mainframe core. Teams get a complete picture of how workloads behave across every tier.
• Unified observability across hybrid cloud. Whether workloads run on IBM z17, in a public cloud, or in containers on OpenShift, Dynatrace brings it all into a single view. No more switching between tools or reconciling data from disconnected systems.
• Security and compliance insights. Dynatrace can surface runtime vulnerabilities, detect unusual behavior, and generate compliance-relevant telemetry to extend the security capabilities built into IBM z17.

IBM z17 and Dynatrace: From infrastructure power to operational intelligence

IBM z17 delivers the infrastructure power: AI at the core, intelligent automation, and ironclad security. Dynatrace delivers the operational intelligence: real-time visibility, automated answers, and end-to-end context. Together, they give teams what they need to harness that power with confidence.

In practice, that means organizations can:

• Accelerate AI adoption on IBM z17 with the observability to validate model performance and catch issues before they impact customers.
• Automate IT operations from the mainframe to the cloud with AI-driven insights that eliminate manual toil.
• Better maintain the reliability, security, and compliance that mission-critical environments demand, with enhanced transparency across every workload.

IBM z17 raises the ceiling for what enterprise infrastructure can do. Dynatrace enables organizations to see everything happening across it and act on operational intelligence in real time.

See what’s possible

If your organization runs workloads on IBM Z, now is the moment to ensure you have the observability to match the platform’s capabilities. IBM z17 is built for the future of enterprise computing. Dynatrace is built to help you operate it.

The post Seeing the core clearly: Dynatrace mainframe monitoring meets IBM z17 appeared first on Dynatrace news.

Enterprise AI is rapidly evolving from standalone models to agentic AI systems, where multiple AI agents collaborate to gather information, reason across data sources, and generate complex outputs. These systems unlock powerful new capabilities, but they also introduce significant operational challenges. Organizations must be able to observe, govern, and optimize AI agents, models, and infrastructure in real time.

Dynatrace helps support this need by providing broad visibility across key layers of the AI stack—from agent orchestration and model inference to GPU infrastructure and enterprise applications. With Dynatrace, teams can monitor AI workflows, understand model behavior, optimize costs, and help improve reliability as agentic systems scale.

Why agentic AI needs full-stack observability

As organizations build GPU-accelerated platforms for AI training and inference, understanding system behavior becomes increasingly complex, with bottlenecks potentially occurring anywhere – from GPU utilization, model latency, token consumption, and downstream service dependencies.

Dynatrace connects these layers through full-stack AI observability, designed to help teams monitor model performance, trace multi-agent workflows, track GPU and infrastructure utilization, detect bottlenecks across AI pipelines, and potentially accelerate troubleshooting with AI-powered root cause analysis.

This unified visibility helps organizations run AI workloads with the same reliability, efficiency, and operational confidence expected from modern enterprise systems.

This unified visibility helps organizations operate AI workloads with improved visibility and operational confidence. By integrating with NVIDIA AI–Q Blueprint and the NVIDIA Agent Toolkit, Dynatrace enriches agent reasoning with high-quality operational telemetry while at the same time helping teams govern and identify opportunities to optimize costs.

How Dynatrace addresses Agentic AI

Dynatrace is designed to assist your team with monitoring infrastructure usage and model behavior and detecting pipeline bottlenecks and token consumption while improving reliability by accelerating troubleshooting and root cause analysis. It also provides a unified view of AI workflows from agent to model down to the infrastructure, allowing organizations to support responsible AI operations, manage cost, improve performance and support agentic workflows at scale.

Every agentic deployment is customized with different agents, tools, models, and data pipelines; therefore, observability is an important capability for understanding how these systems behave in production. The complexity arises as agents interact with multiple enterprise data sources, including:

• internal datasets
• external web and knowledge repositories
• proprietary research systems
• models served through NVIDIA NIM and Nemotron

Dynatrace can serve as operational data source for AI agents that may help improve the quality of generated insights and enable more informed decision-making. With flexible integration across customized AI-Q implementations, this architecture also lays out the groundwork for automated analysis, research, and decision making.

How Dynatrace integrates NVIDIA AI-Q

By combining NVIDIA’s AI-Q Blueprint with Dynatrace AI observability, organizations gain the transparency and operational intelligence needed to govern, optimize, and scale complex AI systems.

Dynatrace integrates into AI-Q environments in two ways.

1. Observability and cost intelligence for Agentic AI workflows

The NVIDIA Agent Toolkit generates lightweight OpenTelemetry traces that Dynatrace ingests to visualize agent workflows and model interactions.

Dynatrace automatically maps the underlying infrastructure supporting AIQ deployments including NVIDIA NIM and Nemotron microservices and enriches telemetry with AI-specific signals such as:

• token usage
• inference latency
• model metadata
• GPU utilization

This provides comprehensive visibility across key components including:

• AI models and inference workloads
• agent orchestration pipelines
• GPU and infrastructure resources
• enterprise data interactions

With these insights, teams can quickly detect performance bottlenecks across agent pipelines, monitor GPU utilization and overall infrastructure health, and identify inefficient model usage. This visibility can help organizations identify cost optimization opportunities associated with AI workloads. Together, these capabilities position observability as important components for building reliable and scalable AI systems.

2. Dynatrace as a high-quality data source for AI agents

Dynatrace can also serve as an operational intelligence source for AI agents.

Through Model Context Protocol (MCP) integrations, Dynatrace exposes telemetry that agents can use in their reasoning workflows, including:

• infrastructure performance metrics
• operational incidents and problems
• deployment and reliability trends
• system behavior and resource consumption

This allows AI agents to incorporate real-time operational insights into their decision-making. Instead of relying solely on external data, agents gain contextual awareness of enterprise systems, which may support more informed outputs Dynatrace ingests NVIDIA Agent Toolkit OpenTelemetry traces, model telemetry, and infra metrics exposing operational context via MCP.

Together, these technologies create a powerful foundation for deploying deep research in the enterprise as reflected in the picture below.

AI-Q use cases

The following are illustrative examples of what becomes possible when AI-Q-based research agents incorporate Dynatrace operational data and insights into their reasoning workflows. While NVIDIA AI-Q is a reference framework rather than a formal certified Dynatrace integration, these scenarios show how agentic research systems could use Dynatrace AI observability to generate richer analysis, identify patterns, and support more informed decisions.

Infrastructure migration analysis

AI agents combine Dynatrace operational telemetry such as performance trends, incidents, and deployment velocity with infrastructure and cloud cost data to evaluate platform migration scenarios (for example, OpenShift to AKS). The system produces data-driven recommendations with quantified tradeoffs to support strategic decisions.

Large-scale incident analysis

By analyzing thousands of historical problems, AI agents can identify recurring patterns, understand infrastructure behavior, and correlate technical issues with business KPIs. This enables deep operational insights and long-form analysis that would be difficult and time-consuming for humans to produce.

AI cost governance and optimization

Enterprises can use observability data from Dynatrace to analyze token consumption, model usage, and inefficient data interactions across AI workloads. Agents can identify patterns and suggest potential optimizations such as more efficient models or improved workflows.

Software delivery and reliability insights

DevOps and SRE teams can use agentic analysis to correlate deployments with incidents, assess build quality trends, forecast reliability risks, and identify engineering priorities—using Dynatrace as the trusted operational data source.

Get started today

• Try the Dynatrace Playground — Explore AI observability with sample data
• Explore NVIDIA integrations in the Dynatrace Hub
• Stream NAT telemetry to Dynatrace — NVIDIA Agent Toolkit OpenTeletemetry guide
• Use Dynatrace as an operational data source — Remote MCP Docs

The post Smarter, safer Agentic AI: Dynatrace observability meets NVIDIA AI-Q appeared first on Dynatrace news.

If you’re running modern applications on AWS, you already have access to more data than ever: metrics, logs, traces, and events. The real advantage comes from turning that data into actionable insights that drive continuous improvement.

Today’s challenge occurs when something breaks; teams still spend too much time connecting the dots. Pulling data from different tools, correlating signals, and trying to figure out what changed. That gap between insight and action is where time is lost, and customer impact grows.

Dynatrace and AWS are working together to close that gap.

With the introduction of AWS DevOps Agent and deeper integrations across Dynatrace and AWS services like Kiro, the experience moves beyond monitoring into AI-powered observability. It becomes a connected system that detects issues, investigates them, and feeds directly into how teams fix and improve software.

How we got here

At AWS re:Invent, AWS introduced DevOps Agent, a new type of AI agent built to investigate and resolve issues across cloud environments. Instead of relying on manual troubleshooting, the agent works continuously across services to understand what changed and why. Dynatrace has been part of that effort since the start. After all, if these agents are going to be effective, they need real production context. That’s the Dynatrace specialty.

That collaboration evolved quickly. Sharing observability data is now a two-way integration. Dynatrace detects and understands issues. AWS DevOps Agent investigates them. The results flow directly back into Dynatrace for a complete view of what happened and what to do next.

The next iteration of the Clouds SRE app

As customers started using the initial implementation, one thing became clear. The foundation was there, but there was an opportunity to make the experience more streamlined, more transparent, and more aligned with how teams actually operate at scale.

With the introduction of the Clouds SRE app, that experience has evolved in a meaningful way.

Instead of requiring users to manually configure multiple workflows, onboarding is now guided. Teams can get started faster without needing to define everything upfront. What used to take several workflows is now simplified into a more focused, purpose-built experience across AWS.

Visibility is another big step forward. Previously, there was limited insight into what agents were doing during an investigation. Now, teams have transparent tracking into agent activity, including approvals, notifications, and the ability to automatically re-run stalled investigations. That shift alone gives teams more confidence in how work is executed.

Routing and management have also become much more intuitive. Rather than managing workflows behind the scenes, teams can now use interaction profiles directly in the UI to control how agents are routed, filtered, and managed. It brings that control closer to where teams already operate.

And importantly, the scope of what agents can do has expanded. What was once limited to investigations now includes mitigation actions as well, allowing teams to move from insight to action without changing context.

Finally, there’s a stronger focus on outcomes. With built-in executive summaries and efficiency metrics, teams can now understand the impact of these workflows in real terms, not just activity.

Taken together, this is a shift from a workflow-centric model to an experience that is guided, observable, and outcome-driven.

From investigation to resolution

Because the integration is two-way, everything stays connected. Findings from AWS DevOps Agent flow directly back into Dynatrace. Root cause, impacted services, and recommended fixes are all visible in a single place.

Teams no longer need to switch between tools or reconstruct the story themselves. The full path from detection to resolution is already laid out.

For teams running distributed systems on AWS, this changes daily operations. Instead of spending time figuring out where to look, teams can focus on fixing the issue and preventing it from happening again.

Bringing that context to developers with Kiro

This is where the story extends beyond operations. Kiro, AWS’s agentic development environment, brings that same production context directly into the developer workflow.

Instead of waiting for a handoff from operations, developers can access real production insights while they are building and fixing code. They can see exactly what failed, understand the root cause, and apply fixes with the same context that was used during investigation.

This removes one of the biggest sources of friction in software delivery. Developers are no longer dependent on separate teams to translate production issues. They are working from the same data in real time.

Now we have a closed loop: Dynatrace detects the issue. AWS DevOps Agent investigates it. Kiro brings that insight directly into the codebase where it can be resolved and improved.

That closes the gap between production and development in a way that was not possible before.

What this means for you

If you are running applications on AWS today, these developments can result in:

• Less time spent correlating data across tools
• Faster and more consistent incident resolution
• Fewer handoffs between operations and development
• Direct access to production context during development

Most importantly, it shortens the feedback loop. Issues are not just detected faster; they can be understood and resolved faster, and improvements can be applied to the code without delay.

Getting started

If you are already using Dynatrace on AWS, the next step is to connect these workflows.

Start by enabling the AWS DevOps Agent integration with Dynatrace to bring automated investigation into your environment. From there, extend that same production context into developer workflows with Kiro so your teams can act on insights directly.

This is the fastest way to move from insight to action and start seeing the value in day-to-day operations.

The post Moving from insight to action: How Dynatrace and AWS are reshaping cloud operations appeared first on Dynatrace news.

Every developer knows the feeling: You’re in your IDE when something breaks. Error rates spike, alerts fire, and suddenly you’re out of the flow. Michael Kobush, Performance Engineer III at the National Association of Insurance Commissioners (NAIC®), wanted to eliminate the gap between development and runtime. Instead of switching tools or waiting on SRE support, NAIC set out to bring production insight directly into the developer workflow.

Let’s take a look at how NAIC embedded real-time observability directly into their development workflow and reduced investigation time to a few minutes.

The problem: Context switching kills developer productivity

Developers lose time the moment they leave their IDE, jumping between views of logs, metrics, and traces simply to understand what has changed.

For NAIC, this friction was slowing down their teams. Developers didn’t have access to production context, creating a dependency on SRE teams whenever investigations were needed. An analysis that should have taken minutes routinely took 45 minutes to an hour. Root-cause identification required manual correlation across multiple systems, a process that was neither scalable nor sustainable.

At Dynatrace Perform 2026, Kobush demonstrated how his team uses Kiro and Dynatrace at NAIC: Real-time observability in your IDE: How NAIC uses Kiro powers to drive developer productivity

The solution: Kiro powers and intelligent observability

Kiro is AWS’s agentic AI-powered IDE that takes a spec-driven approach to software development by turning natural language prompts into structured requirements, architecture designs, and implementation tasks to carry code from prototype to production.

NAIC installed the Dynatrace power for Kiro, one of Kiro’s installable powers that
dynamically connect domain-specific tools and context to the agent. Once connected, Kiro gives developers and AI agents access to Dynatrace data and insights, helping them pinpoint root causes and receive remediation recommendations directly in their workflow.

No switching between tools. No waiting on another team.

The aha moment: Root-cause analysis in minutes, not hours

The first prompt NAIC ran after connecting Kiro to Dynatrace set the tone for everything that followed. Kobush typed a single line into Kiro: “Tell me about problem P-18576.”

Within 30 seconds, Kiro returned a full problem summary with details and recommendations, pulling everything from Dynatrace automatically. Then, he pushed further: “Give me a really deep dive root-cause analysis of what happened.”

In under two minutes, Kiro returned a full root-cause analysis correlating telemetry, infrastructure signals, historical incidents, and the current problem from Dynatrace into a structured response that included:

• An executive summary
• Detailed problem context
• Infrastructure analysis
• Technical root-cause analysis
• Remediation strategies
• Conclusions and next steps

A preliminary assessment that would have previously taken 45 minutes to an hour was now done in minutes. More importantly, it wasn’t just faster; it gave the team a clear, connected view of how services, infrastructure, and dependencies contributed to the issue.

Beyond root cause: Automation across the entire workflow

What makes this more than just a faster diagnostic tool is how NAIC extended Kiro’s capabilities to automate the full incident response workflow.

Using Kiro’s steering files feature, NAIC configured Kiro to automatically generate a structured Markdown file whenever a root-cause analysis was completed. That file includes:

• Relevant DQL queries used during the investigation
• Direct links to the Dynatrace dashboards and data sources that surfaced the issue
• A clear summary of findings

With a Targetprocess MCP also connected, Kiro can take that analysis and populate a ticket directly, automatically loading all relevant context and sending it to the development team. For NAIC, this means the handoff from investigation to remediation is essentially hands-off. This level of automation doesn’t just save time; it creates consistent, repeatable workflows with built-in guardrails. Every incident gets the same structured, data-rich documentation, regardless of who’s investigating it or when.

This isn’t just about faster incident response. It changes how teams build and release software—giving developers immediate feedback on how their changes behave in real environments.

Proactive alerting: Catching problems before they crash

Root-cause analysis after the fact is valuable. With observability embedded directly into the workflow, teams can detect issues earlier in development and respond faster in production, closing the gap between building and operating software.

After noticing that a specific process had crashed, Kobush asked Kiro to set up an alerting profile that would trigger both before the crash, based on stress signals visible in the logs, and at the point of the crash. Kiro analyzed historical log data, identified pre-crash indicators, and built the alert profile automatically.

The result: NAIC’s team now receives early warning signals before a process fails, giving engineers time to intervene rather than react.

This shift from reactive to proactive operations is central to what the Dynatrace and AWS partnership enables. When observability data is embedded in the developer workflow rather than siloed in a separate platform, the entire engineering organization is better equipped to prevent incidents, not just resolve them.

Debugging a sneaky production bug

Perhaps the most telling story from NAIC’s experience with Kiro occurred during a routine error-rate investigation.

An application error rate had increased unexpectedly. Kobush asked Kiro to investigate. Two minutes later, Kiro identified the culprit: A developer had left debug code in the development environment, and it had made its way into production. Every time a user triggered that code path, it threw errors.

When Kobush sent the Markdown report to the developer, the response was immediate: “How did you find that? I’ve been looking for that.”

Kiro leveraged correlated logs, traces, systems context, and historical behavior from Dynatrace to pinpoint exactly where the issue originated.

Start embedding observability into your development workflow

NAIC’s experience highlights a broader shift: When developers, AI assistants, and systems all operate from the same runtime context, debugging becomes faster, releases become safer, and teams spend less time chasing issues and more time building.

The broader message from Kobush is simple: “I’m not a developer. I have a degree in biology and a minor in chemistry… But this, to me, is a game changer in the observability space. I can do things in seconds that would take me hours.”

The productivity gap between observability data and developer action is a solvable problem.

For DevOps engineers, SREs, and platform teams looking to accelerate incident resolution, reduce context switching, and move from reactive troubleshooting to proactive operations, the Dynatrace and Kiro integration offers a practical, immediately actionable path forward.

For developers, this means fewer interruptions, faster answers, and the ability to stay in flow, even when issues arise.

The post From reactive to proactive: How NAIC embedded AI‑powered observability directly into the IDE appeared first on Dynatrace news.

Your on-call engineer is looking at intermittent HTTP 500s from a service running on a virtual machine that was perfectly healthy twelve hours ago. Nothing changed in the application. No recent deployments. Logs show connection timeouts — but only under load, and only to external services. The app itself looks fine.

This is the signature of SNAT port exhaustion: Azure’s most reliably invisible production failure. It masquerades as an application bug while the root cause sits quietly in your networking configuration. Left unresolved, it degrades under load, confuses your developers, and burns investigation hours on symptoms instead of causes.

With the general availability of Azure Cloud Platform Monitoring, Dynatrace now ships a pre-built alert that catches this exact failure — before your engineers are paged, or the moment they are. This is the story of how that plays out.

What is SNAT exhaustion?

Source Network Address Translation (SNAT) is how Azure allows resources in a virtual network to make outbound connections to the internet without exposing a public IP on each instance. When a backend instance behind an Azure Load Balancer initiates an outbound TCP connection, the load balancer performs SNAT: It maps the source IP and port to one of its frontend IPs and a port from a shared pool.

The trap: Azure allocates a limited number of SNAT ports per backend instance. The exact number depends on how many instances are in the backend pool — smaller pools get more ports per instance (up to 1,024), while larger pools get fewer (as low as 32 per instance). Each active outbound connection consumes one port for the duration of the connection plus its TIME_WAIT period — typically up to four minutes. Under normal load, this is plenty. Under burst traffic — or when an application makes many short-lived outbound calls without connection pooling — those ports vanish fast.

When the pool is exhausted, new outbound connections fail with a generic network error. The application logs a 500 or a timeout. Developers stare at their code. The infrastructure looks healthy in Azure Monitor… unless you know exactly which metric to watch and where.

12:03 PM — The alert fires

At 12:03 PM, Dynatrace fires a problem notification: “Azure Load Balancer SNAT Port Exhaustion High” — one of the pre-shipped health alerts that comes with Azure Cloud Platform Monitoring.

The alert details show the affected resource: retail-snat-lb, a production Load Balancer in the prod-eastus subscription, with SNAT connection counts climbing rapidly toward the allocated port ceiling. The problem card surfaces the backend IP addresses approaching exhaustion — in this case, two instances already above 75% of their allocated ports — alongside the duration, the severity trend, and a direct link into the Clouds app to investigate.

What’s going on?

Opening retail-snat-lb in the Clouds app, the metrics make the problem immediately clear. UsedSnatPorts is peaking at 256 — against an allocated ceiling of 128 ports per backend instance. That is 100% utilization, and it is not a one-off spike. The pattern repeats with every traffic wave hitting the backend.

Metrics tell the story, but logs confirm it. With Azure resource (diagnostic) logs ingested into Dynatrace, we can corroborate what the SNAT metrics are showing.

The last 10 error and warning log entries confirm it: the Azure Load Balancer is experiencing SnatPortExhaustion.

Azure Cloud Platform Monitoring continuously maps your cloud topology and persists it in Smartscape^® as nodes. Dynatrace discovers relationships between Azure resources and creates Smartscape edges for the relevant nodes.

From the related resources tab, we can drill into the outbound rule configuration for this Load Balancer.

We can see that allocatedOutboundPorts is currently set to 128. This is the custom number of SNAT ports which are pre-allocated per backend instance for outbound connections. If a VM uses all its allocated ports — due to many concurrent or short-lived outbound connections — new connections fail with SNAT exhaustion errors.

Increasing allocatedOutboundPorts reduces this risk, but there is a tradeoff: More ports per instance means fewer instances per frontend IP. With a single frontend IP (64K ports), setting 1,000 ports per instance supports roughly 64 instances; setting 128 ports per instance supports roughly 500 instances.

But what backend instances are serving traffic? We can query Smartscape topology using DQL to understand exactly which Azure Network Interfaces are associated with the Load Balancer’s backend pool.

We can see there are two Azure Virtual Machines whose network interfaces are registered in the backend pool, both making outbound calls through a single Azure Load Balancer.

This matters because SNAT exhaustion is a per-instance problem. With two VMs in the backend pool, Azure pre-allocates 128 SNAT ports to each instance via the outbound rule. Under load, both VMs are independently hitting their own port ceilings — which is exactly what the metrics are showing. The topology makes this architecture immediately visible, without navigating Azure Portal resource groups or drawing a diagram by hand.

Observability superpowers

With the root cause identified on a single Load Balancer, the next question is obvious: Is this the only one at risk?

This is where Dynatrace Assist comes in — and with Azure-specific agentic skills arriving in an upcoming Dynatrace release, it gets even more powerful. Assist is your natural language gateway into Dynatrace Intelligence — the agentic operations system that brings the full platform together. Instead of switching between dashboards, writing queries by hand, or clicking through Azure Portal subscription by subscription, you ask a question in a conversational interface. Assist draws context from Grail™ and maps relationships through Smartscape to deliver grounded, data-backed answers.

Once these skills are generally available, asking Assist to “list all Azure Load Balancer outbound rules sorted by allocatedOutboundPorts” will generate a DQL query that scans configurations across every connected Azure subscription in seconds — the kind of cross-subscription audit that would take hours of portal hopping. The result instantly surfaces every Load Balancer with details of their SNAT configuration, turning a single resource investigation into an environment-wide remediation sweep.

Additionally, you’ll be able to quickly understand which Azure resources are affected for a load balancer that has this custom SNAT configuration applied by asking, “For load balancer retail-snat-lb which backend instances are affected by this configuration?”

“Our Madrid MultiCloud strategy is based on a unified observability office powered by the Dynatrace platform, which allows us to anticipate problems, increase agility, and scale our services. This approach is key to driving the City Council’s digital transformation and delivering better services to citizens. Specifically, Dynatrace Cloud Operations for Azure helps us detect early warning signs, reduce operational noise, and respond automatically when incidents occur, allowing our development teams to focus on the continuous delivery of new features.”

– Mónica Romero Domínguez, Head of Observability and Monitoring, IT Agency at Madrid City Council

Two options, one outcome

Fixing SNAT exhaustion on an Azure Load Balancer is straightforward once you know what you are dealing with.

Option A: Attach a NAT Gateway. A single NAT Gateway with one public IP address provides 64,512 SNAT ports. With multiple IP addresses, you multiply that ceiling further. NAT Gateway completely decouples outbound connectivity from the Load Balancer’s frontend IP allocation and is the recommended pattern for workloads with significant outbound traffic.

Option B: Define explicit outbound rules. If NAT Gateway is not feasible, add explicit outbound rules to the Load Balancer and associate multiple frontend IP addresses. Azure allocates ports proportionally — more frontend IPs means more ports per backend instance.

Either approach will cause the SNAT connection utilization to drop back to baseline. The recovery is visible immediately in the Dynatrace dashboard, and the pre-shipped health alert resolves automatically once utilization falls below the critical threshold.

Azure observability at scale

The investigation above was possible because retail-prod-eastus was already being monitored — not because someone remembered to configure it. Onboarding happens once at the Azure Management Group level, and Dynatrace automatically discovers every subscription beneath it, including subscriptions created after the initial connection. Authentication uses federated credentials: there are no shared secrets to store, rotate, or let expire.

Everything seen during this investigation — the pre-shipped alert, the SNAT metric, the topology, the VM logs — arrived without any Dynatrace components running inside Azure. Metrics, topology, logs, and events are polled directly by the Dynatrace platform. This replaces the previous approach, which required self-hosting the Dynatrace Azure Log Forwarder for log ingestion — a component that needed to be deployed, scaled, and maintained inside each Azure environment.

The SNAT metric that surfaced this problem was available out of the box, part of the Dynatrace curated metric collection set for Azure Load Balancers. Coverage is not limited to what Dynatrace has pre-selected: Auto-discovery can ingest all metrics available for any Azure service, or any Azure Monitor native platform metric can be collected on demand — without waiting for Dynatrace to add explicit support. The resource inventory underlying the Smartscape topology is built from Azure Resource Graph, scanning subscriptions continuously so the entity map reflects the environment as it actually is.

Every ingested signal — metric, log entry, event — is automatically linked to the Smartscape entity representing the Azure resource that produced it. The reason the SNAT spike, the VM timeouts, and the Load Balancer topology appeared together in the same investigation is that they are stored in the same context. There is no join to write, no dashboard to cross-reference. The data arrives pre-correlated.

Smartscape^®, Grail™, and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies. All other trademarks are the property of their respective owners.

The post The silent network killer: Preventing Azure SNAT exhaustion with Dynatrace appeared first on Dynatrace news.

AI is changing how enterprises build, operate, and scale. It is also accelerating how quickly partners need to engage, differentiate, and deliver value.

In this environment, speed and alignment matter. Partners and Dynatrace must operate as one team: sharing information, reducing friction, and executing with clarity.

At Amplify 2026, the Dynatrace Partner Sales Kickoff, we introduced a set of updates designed to support that shift. These updates expand access to enablement, simplify how partners get started, and strengthen go-to-market execution to help our shared ecosystem move faster and deliver better outcomes for our customers.

Expand shared enablement across the ecosystem

As the market accelerates, access to timely, relevant information is critical.

Dynapulse, historically an internal sales training and enablement program, is now available to partners. This includes ongoing briefings, product updates, competitive insights, and market context.

Expanding Dynapulse helps ensure that partners and Dynatrace teams are aligned on priorities and prepared for customer conversations. It also reinforces a consistent approach to how the market is addressed, grounded in shared knowledge and a common strategy.

Accelerate growth with a simplified entry point

Faster engagement often starts with simpler entry points.

Dynatrace Core introduces a packaged offering designed to reduce early-stage friction. It provides a more straightforward way to begin working with customers while maintaining a path to expand into the full Dynatrace platform over time.

This approach supports earlier engagement, faster decision-making, and more efficient progression through the sales cycle.

Strengthen how the ecosystem competes and wins

Clear positioning and access to competitive insights are essential in a fast-moving market.

In response to partner feedback, Dynatrace is launching new competitive content on the Partner Portal. These new resources centralizes competitive intelligence and guidance, making it easier to access and apply in active opportunities.

The focus is on improving consistency in how Dynatrace is positioned, enabling more confident conversations, and supporting stronger execution in the field.

Sustain momentum through ongoing enablement

In a dynamic market, ongoing alignment is as important as initial enablement.

Partner PowerUp continues to serve as a midyear executive enablement series happening from August through September 2026. The EMEA event will be on October 6-7, 2026, and the APAC event will be on October 20-21, 2026. These sessions provide a forum to align on strategy, share priorities, and identify new opportunities as conditions evolve.

Ongoing engagement helps maintain momentum and ensures that the partner ecosystem remains informed and connected.

Moving forward, together

As the market continues to change, the ability to move together quickly and effectively becomes a key advantage. The updates shared at Amplify reflect a broader evolution of the Dynatrace partner ecosystem for the AI era. Reducing friction and improving alignment will enable consistent, high-value execution across the ecosystem.

At Amplify 2026, we honored partners with our annual Partner Impact Awards for exceptional customer and business impact. This year’s winners are helping drive growth and stronger customer outcomes. Congratulations to all the winners!

The post Scaling the partner ecosystem for AI: What’s new from Dynatrace Amplify 2026 appeared first on Dynatrace news.

Most enterprises have moved past the deployment problem. The harder question is what those workloads are doing in production: where GPU spend is going, how agent chains are behaving, and whether compliance teams can answer when regulators ask. When the answers aren’t clear, the consequences land fast and are rarely contained to one team.

That’s why Dynatrace is joining the Dell Technologies AI Ecosystem Program, bringing full-stack AI and LLM observability natively into a broad and integrated AI infrastructure ecosystem. Dell delivers the validated, integrated infrastructure to run AI at scale. Dynatrace brings the observability, automation, and governance to operate it with confidence, with visibility from GPU infrastructure to model behavior to end-user experience. Together, they give enterprises the control to match the scale they’ve already built.

The real challenge: AI at enterprise scale

Running AI in a pilot is very different from running it at scale across the business with real users, regulated data, and demanding SLAs. As we’ve worked with enterprises across industries, these failure patterns come up repeatedly:

Cost

As enterprises scale AI, costs spiral rapidly and unpredictably across model providers, GPU clusters, and inference APIs without clear line of sight into what is driving spend or whether it’s delivering value.

Observability gaps

Traditional monitoring tools weren’t built for AI pipelines. Fragmented observability across GPU clusters, orchestration layers, and inference APIs creates blind spots while LLM latency and token throughput fluctuations under load remain difficult to diagnose and even harder to predict.

Agentic complexity

Multi-step agent workflows introduce cascading failure modes. A silent error in one tool call can corrupt downstream decisions across the entire chain.

Compliance & governance

Enterprises need continuous monitoring to detect model drift, hallucinations, and unsafe outputs before they impact end users. Regulated industries need audit trails, data governance, and behavioral monitoring that most AI monitoring bolt-ons simply weren’t built for.

These aren’t edge cases. They’re the norm. And they’re the reason so many AI initiatives stall between pilot and production.

“Agentic AI changes what observability has to do. You’re no longer watching one model respond to one prompt. In agentic AI, every transaction can be unique, and you’re tracing chains of autonomous decisions across dozens of tools and services. That’s the problem Dynatrace was built to solve and Dell AI Factory is exactly the foundation enterprises need to take AI to production at scale.”

— Steve Tack, Chief Product Officer, Dynatrace

Scale AI workloads with confidence

Dynatrace can be integrated into Dell AI Factory environments to cover end-to-end observability of agentic AI and LLM workloads. The goal is straightforward: no blind spots, no surprises, and no manual investigation when something goes wrong. Here’s what that looks like in practice:

• Unified AI observability to monitor the AI stack. Prompts, Model calls and downstream services, in a single platform that replaces the fragmented tooling most teams rely on today.
• Automated prevention and remediation with Dynatrace Intelligence®. When AI workloads behave unexpectedly, Dynatrace Intelligence detects anomalies in real time and triggers automated remediation to minimize or eliminate downstream consequences.
• End-to-end agentic AI tracing. Distributed tracing across multi-step agent chains, tool calls, RAG pipelines, and external integrations gives teams visibility into how AI agent decisions are made and where they go wrong.
• Automatic topology mapping with Smartscape®. Maps every component in your Dell AI Factory environment, showing in real time how infrastructure, services, and AI models depend on and affect each other.
• Built-in data governance and audit trails. Track data flows, model decisions, and AI service behavior with governance capabilities designed for regulated industries not retrofitted to them after the fact.
• Faster resolution with Dynatrace Assist. Natural language querying and AI-generated remediation recommendations help operations teams resolve issues faster, even without deep AI infrastructure expertise.

Built for the industries where AI is becoming mission critical

AI is no longer an experiment. It’s become core infrastructure for the world’s most demanding enterprises, embedded in the decisions, workflows, and customer experiences that keep businesses running. When AI is mission critical, a failure isn’t a learning opportunity; it’s a negative business impact. Tolerance for poor visibility, unexplained latency, or untraceable decisions drops to zero. That’s precisely where Dynatrace AI Observability comes in, giving teams the visibility, control, and real-time intelligence to keep AI running when it matters most.

“The enterprises winning with AI aren’t running one model in one department. They’re operationalizing AI across the business. Dynatrace joining the Dell Technologies AI Ecosystem Program gives those customers the observability foundation to expand AI workloads on Dell infrastructure with the reliability, governance, and efficiency that enterprise-scale demands.”

— Brad Maltz, Senior Director of AI Solutions, Dell Technologies

What this means for joint customers

For organizations deploying on Dell AI Factory infrastructure, the combination of Dell’s validated hardware and software stack with Dynatrace’s intelligent observability platform means:

• Scale with confidence. Expand production AI across the business without losing visibility or control.
• Higher AI reliability. Proactive anomaly detection surfaces issues early; moving teams from reactive firefighting to confident operations.
• Lower risk at scale. Broad stack visibility reduces the unknowns that make executive teams cautious in moving AI to production at scale.
• Improved ROI on AI investment. When AI workloads run efficiently and every GPU hour is visible, teams can continuously optimize performance and cost.

End-to-end observability isn’t a nice-to-have for AI. It’s a prerequisite for trust, and trust is what turns AI investments into business outcomes. We’re proud to bring that capability to the Dell AI Factory ecosystem, and we’re excited about how this deepening of our relationship with Dell can unlock incredible value for our joint customers on their AI journeys.

The post Scaling enterprise AI with confidence: Dynatrace joins the Dell Technologies AI Ecosystem Program appeared first on Dynatrace news.

Red Hat Summit is Red Hat’s flagship event for IT leaders, platform engineers, developers, and operations teams building and scaling modern hybrid-cloud environments. This year’s Summit puts a focus firmly on helping enterprises operationalize AI at scale—powered by open hybrid-cloud platforms and automation—to bridge today’s IT realities with the next generation of innovation.

Dynatrace at Red Hat Summit 2026

Dynatrace is at Red Hat Summit 2026 to help platform and operations teams gain precise, real‑time insight across OpenShift‑based environments and the automation layers that run on top of them.

Red Hat delivers the open hybrid-cloud platforms organizations depend on to build and run applications anywhere. Dynatrace complements this with AI‑powered observability that automatically discovers applications, services, infrastructure, and dependencies across dynamic OpenShift environments, turning telemetry into trusted, actionable answers.

Together, Dynatrace and Red Hat help teams:

• Monitor and optimize cloud‑native, virtualized, and containerized workloads
• Support AI and OpenShift AI environments with continuous performance insight
• Enable automation and remediation workflows with accurate, real‑time context

What will Dynatrace demonstrate at Red Hat Summit 2026?

At Red Hat Summit, Dynatrace will showcase how observability and automation work together in practice across OpenShift, hybrid cloud, and AI‑driven environments.

You can expect:

• Live demos showing Dynatrace monitoring OpenShift clusters, Kubernetes services, and AI‑enabled applications
• Deep dives into how Dynatrace integrates with Red Hat OpenShift AI, OpenShift Virtualization, OpenShift, and Ansible Automation to support performance, reliability, and automation workflows
• Insights from Dynatrace experts on reducing operational blind spots in fast‑changing, containerized environments

We’ll also participate in a series of speaking sessions. Don’t miss out. Secure your place today.

Monday, May 11

• Register for a joint Red Hat & Dynatrace Session – 2:40 PM – 3:00 PM EDT – Room B404 – Level 4: Lightning Talk | Observability for LLM inference: Telemetry at scale with llm-d

Tuesday, May 12

• Register for a Dynatrace session – 2:00 PM – 2:20 PM EDT – Expo Hall – Discovery Theater 1 Lightning Talk | The observability platform for OpenShift AI

Key integration themes to explore at the event

Red Hat Summit 2026 brings together several technology trends that are converging rapidly in enterprise IT. Dynatrace supports these priorities through deep integration with Red Hat platforms.

Observability for AI‑enabled workloads

As organizations adopt OpenShift AI, observability becomes critical to maintaining performance, reliability, and cost control. Dynatrace helps teams monitor model‑driven applications alongside the full application and infrastructure stack.

OpenShift Virtualization

Dynatrace integrates with Red Hat OpenShift Virtualization to provide clear visibility and root cause insights in the cluster health using KubeVirt metrics with built-in dashboards and alerts. This reduces manual monitoring, speeds up issue detection and resolution, and helps optimize capacity for virtual workloads. In addition, virtualized workloads can gain full stack observability and security insights with Dynatrace OneAgent.

Observability for OpenShift and hybrid cloud

Dynatrace provides automatic discovery and real‑time topology mapping across OpenShift clusters, workloads, and services, helping teams understand how changes impact application behavior and user experience.

Automation with confidence

Through integration with Red Hat Ansible Automation, Dynatrace enables event‑driven automation and remediation workflows, powered by precise root cause analysis rather than static alerts.

By combining Dynatrace, Red Hat Ansible Automation Platform, with ServiceNow, we deliver a closed-loop AIOps solution that connects intelligent monitoring, ITSM, and automated remediation. This integration reduces downtime, accelerates resolution, and enables more proactive, self-healing IT operations.

Join us at Red Hat Summit 2026

Dynatrace continues to invest in deeper integrations across the Red Hat ecosystem, focusing on observability that supports automation, AI, and hybrid cloud at enterprise scale.

Whether you’re modernizing existing applications or building new AI‑enabled services, Dynatrace and Red Hat together provide the foundation for intelligent, automated operations.

Additional resources:

• Dynatrace Hub – Red Hat OpenShift
• Dynatrace Hub – Red Hat Ansible
• Blog – How Dynatrace Kubernetes Monitoring powers smarter insights

The post Red Hat Summit 2026: Powering intelligent, automated operations across hybrid cloud and AI workloads appeared first on Dynatrace news.

Dynatrace is excited to be a Gold sponsor at ServiceNow Knowledge 2026—ServiceNow’s premier annual conference—where IT leaders, practitioners, and partners converge to discuss the future of enterprise operations. Being here matters because this event is at the forefront of how AI, automation, and agentic workflows are transforming the IT landscape. Held in Las Vegas from May 5–7, 2026, Knowledge 2026 is especially significant as it focuses on the shift from reactive incident response to autonomous IT operations.

Dynatrace at ServiceNow Knowledge 2026

Join us onsite to see how our joint innovations with ServiceNow help enterprises accelerate agentic AI initiatives, break down operational silos, and automate with confidence. We’ll also showcase a recent update, featuring a Now Assist and Dynatrace MCP Server integration, designed to support “zero-outage” outcomes at enterprise scale.

ServiceNow delivers the workflows, orchestration, and enterprise control plane used by IT teams worldwide. Dynatrace complements this with AI‑powered observability that delivers precise root cause, dependency mapping, and business impact in real time. Together, Dynatrace and ServiceNow help organizations move from reactive operations to proactive—and ultimately autonomous—outcomes.

Throughout the event, Dynatrace experts will be on site to share practical guidance and real‑world examples of how organizations are accelerating autonomous IT operations initiatives.

You can expect:

• Live demos showing how Dynatrace integrates with ServiceNow to enrich incidents, workflows, and CMDB data with precise root cause answers, dependencies and business impact, all in real‑time.
• Conversations with Dynatrace specialists on reducing alert noise, accelerating remediation, and scaling automation.

We’ll also be highlighting existing integrations and resources that customers can use today through the ServiceNow store and the Dynatrace Hub.

Don’t miss out. Secure your spot now by registering for one of our exciting activities. Check out the schedule below and sign up today:

Tuesday, May 5

• Register for a joint Accenture, Dynatrace Session – 1:30PM – 2:00 PM PDT, Wynn, Mouton 1: “Reinvent IT Ops: From Observability to Autonomous Operations”
• Join us at the Dynatrace booth – during the Expo Welcome Reception – 5:00PM – 6:00PM – for wine, cheese and networking – sponsored by AHEAD.

Wednesday, May 6

• Register for our Expo Session – 12:00 – 12:15 PM PDT, Theater 4, Expo Hall: “AI that acts: Enabling autonomous IT with Dynatrace and ServiceNow”
• Register for the DXC & Dynatrace Customer Lunch – 11:30 AM, Sugarcane – The Venetian Join Dynatrace, DXC and ServiceNow for a discussion around “Taking the Guesswork out of Autonomous IT Operations”
• AHEAD Sip and Socialize reception – 6:00 – 9:00 PM, LAVO at The Venetian
• Carahsoft Networking Reception – 7:00 – 10:00 PM, TAO Beach Dayclub
• DXC, Boomi, and Dynatrace customer reception – 7:00 – 9:30 PM, Sugarcane – The Venetian

New Dynatrace and ServiceNow integration enhancements

The latest Dynatrace and ServiceNow integration enhancements provide teams with richer context and faster paths from detection to action, so automation is driven by answers, not guesses.

Key enhancements include:

• Now Assist updates to drive autonomous operations by enabling ServiceNow users to investigate alerts using contextualized insights from Dynatrace through existing AI agent connections and through the Dynatrace MCP Server to reduce alert fatigue and solve problems quickly and securely without “swivel-chairing” between solutions.
• Real-time digital experience context, including real user monitoring (RUM) signals, to help ServiceNow teams understand customer-impacting issues with greater precision.
• Dynatrace event/anomaly context streamed to ServiceNow, including “problem events” and enriched ITOM alerts with severity, root cause, and impacted entities to improve triage speed and operational efficiency.
• Continued joint roadmap development, building on the companies’ multi-year collaboration and ongoing integration enhancements and ServiceNow CMBD enrichment informed by real-world use and customer needs.

Take the next step in your transformation

ServiceNow Knowledge 2026 is your opportunity to explore how observability, automation, and AI come together in real-world enterprise environments. Check out Jay Livens and Brian Chandler present the session, “AI That Acts: Enabling Autonomous IT With Dynatrace and ServiceNow,” to learn how you can simplify operations, enhance enterprise resilience, and achieve autonomous IT operations while accelerating remediation.

Additional resources:

• Webinar – Autonomous Operations: Better together with ServiceNow
• Customer Story – BT Digital transforms service operations with Dynatrace to drive improved customer experience
• Blog – Intelligence in, intelligence out: How Dynatrace and ServiceNow are powering autonomous operations
• Blog – Accelerate your autonomous IT operations journey with Dynatrace and ServiceNow integrations
• Blog – Dynatrace Intelligence at the core of autonomous operations

This blog may contain forward‑looking statements regarding product capabilities, integrations, and anticipated enhancements. These statements are for informational purposes only and are not guarantees of future functionality. Product features and timelines are subject to change at Dynatrace’s discretion.

The post ServiceNow Knowledge 2026: Accelerating intelligent automation at scale appeared first on Dynatrace news.

A new chapter of digital innovation is underway.

With the emergence of agent-driven services and generative capabilities, organizations now have new ways of building, operating, and improving digital products. These technologies open the door to richer customer experiences, faster feedback cycles, and continuous improvement powered by real-time insight.

At AWS Summits, Dynatrace is showcasing how our work with Amazon Web Services can help teams explore ways to turn agentic and generative innovation into measurable outcomes. Together, our teams have been delivering platform capabilities and deep integrations that allow customers to take advantage of these new AWS services at scale.

If you are attending an AWS Summit, stop by the Dynatrace booth to see agentic and generative innovation in action.

Turning new AWS capabilities into real-world outcomes

Dynatrace and AWS share a common mission to support customers as they innovate more efficiently and work toward better digital experiences across cloud-native and modern application environments.

Through close collaboration, Dynatrace has enabled customers to adopt new AWS services with confidence from the moment they become available. Dynatrace provides the intelligence layer that connects data across applications and infrastructure, allowing teams to better understand their user behaviors and application interactions, which can help inform business decisions and improvements.

As AWS introduces agent-based services and managed generative platforms, Dynatrace helps customers fully realize their value by connecting insight to action.

Bringing AWS-native signals into one view with Dynatrace Clouds app

A key part of unlocking this opportunity on AWS is access to the native signals that AWS services already produce.

Dynatrace Cloud Operations brings Amazon CloudWatch metrics and AWS service data points directly into the Dynatrace platform for a richer context. This allows teams to work with AWS-native telemetry alongside application behavior, user experience, and business signals in a single view.

With the Clouds app, customers can see CloudWatch metrics from AWS services in context with applications and workloads, connect AWS service signals using Dynatrace SmartScape® topology, and apply consistent analysis and automation across AWS native data and Dynatrace collected data.

At the AWS Summits, Dynatrace will demonstrate how the Clouds app enables shared visibility across AWS environments by connecting managed Dynatrace environments to Dynatrace SaaS and AWS services. This shared visibility becomes especially powerful when combined with agent-driven services and Amazon Bedrock, allowing agents and automated workflows to operate using trusted AWS data together with Dynatrace Intelligence.

See agentic and generative innovation in real‑world cloud environments

At the AWS Summits, Dynatrace will demonstrate how AWS services and Dynatrace Intelligence work together to enable a new way of building and evolving digital products. Live demos and hands-on conversations will show how insight flows naturally across teams and systems.

AWS DevOps Agent and continuous product improvement

With AWS DevOps Agent and Dynatrace, teams can understand in real time how changes impact users and business outcomes. Dynatrace provides trusted context from production environments that feed into agent-driven workflows, helping teams learn faster and deliver higher quality experiences with every release.

Amazon Bedrock and Bedrock AgentCore at real-world scale

With Amazon Bedrock and Bedrock AgentCore, teams are building agents that reason across information and act on behalf of users. Dynatrace enables customers to understand how these services behave in real-usage scenarios and scale generative capabilities within their products.

Kiro and Kiro Powers accelerating innovation from code to customer

Dynatrace connects live production insight back into agent-assisted development workflows, allowing teams to validate ideas using real usage and performance signals and shorten the cycle from idea to impact.

Dynatrace MCP connecting context across agent-driven systems

Dynatrace Model Context Protocol technology enables secure and scalable sharing of system context across agent-driven ecosystems. At the summit, see how MCP connects managed Dynatrace environments, Dynatrace SaaS, and Amazon Bedrock to support coordinated-intelligent behavior at cloud scale.

Amazon SageMaker and continuous product improvement

With Amazon SageMaker and Dynatrace, teams gain insight into how models perform in live environments and refine AI-driven features over time based on real-usage patterns.

Join us at the AWS Summits

Dynatrace and AWS have been working together to deliver the platform capabilities, integrations, and shared intelligence that support this opportunity in real‑world environments today.

We’ll be onsite at multiple AWS Summits across North America, EMEA, and APJ. Visit the Dynatrace booth to see live demos and connect with experts shaping the next generation of digital products. See us at an AWS Summit near you.

Dynatrace, Dynatrace SmartScape®, Dynatrace Model Context Protocol, Dynatrace MCP, and Dynatrace Intelligence are trademarks or registered trademarks of the Dynatrace, Inc. group of companies. Amazon Web Services, AWS, Amazon Bedrock, Amazon SageMaker, and CloudWatch are trademarks of Amazon.com, Inc. or its affiliates. All other trademarks are the property of their respective owners.

The post Unlocking a new era of digital innovation with Dynatrace and AWS appeared first on Dynatrace news.

Editor’s note

At Google Cloud Next 2026, the future of digital operations and artificial intelligence is on full display. The mandate for enterprises is clear: Successfully scaling AI requires a foundation of intelligent observability.

The momentum behind the Dynatrace collaboration with Google Cloud has never been stronger. That’s why we’re proud to be a Velocity sponsor for the event. We are working with Google Cloud to help organizations accelerate AI adoption through deep trust, robust automation, and unified observability. By transforming operational complexity into a strategic advantage, we empower DevOps, SREs, and platform engineering teams to innovate faster and secure their cloud environments.

As Google Cloud Next kicks off in Las Vegas, read our guide for the latest insights on how Dynatrace and Google Cloud are helping to shape the future of autonomous operations.

— Jay Snyder, SVP of Global Partners and Alliances, Dynatrace

The latest Dynatrace news and announcements from Google Cloud Next

The rapid rise of generative and agentic AI is reshaping how teams build, run, and scale applications—and it’s adding new layers of complexity along the way. Dynatrace helps teams make sense of that complexity by providing clear, automated insights across their Google Cloud environments, built on top of Google Cloud’s secure-by-design infrastructure.

Here’s the latest news from Dynatrace at Google Cloud Next:

• The Dynatrace for Gemini Enterprise Agent is now live on Google Cloud Marketplace. This agent provides a clear, efficient way for enterprises to discover, evaluate, and deploy validated AI agents at scale. This means teams can bring Dynatrace Intelligence into their Gemini agents faster, with less friction and without custom integration work.
• Dynatrace and Google Cloud Assist Investigations now work together to enable autonomous, end‑to‑end incident response. Dynatrace detects issues, understands their root cause through deterministic AI, and automatically triggers an Assist Investigation with comprehensive production context — topology, traces, dependencies, and business impact. Google Cloud’s agent then correlates this with its own telemetry and change history to pinpoint the failing component and recommend the next best action, all surfaced directly in Dynatrace. The result is a more seamless path from symptom to fix without war‑room back‑and‑forth or tool‑switching.
• The Dynatrace enhanced Clouds App gives teams instant, unified visibility across their entire Google Cloud environment through a fully managed, agent-free integration that automatically ingests metrics, logs, and events without custom pipelines or extra infrastructure. It also supercharges the tags organizations already use in Google Cloud, applying them automatically to drive cleaner ownership, smarter routing, and reduced operational noise. This helps teams move from reactive firefighting to proactive, preventative operations: detecting issues earlier, automating root-cause analysis, and powering self-healing workflows while optimizing resources and infrastructure over time across the entire Google Cloud ecosystem.

“Google Cloud is advancing a new era of agentic operations, and partners like Dynatrace are essential to making that vision real for customers. Together, we’re enabling enterprises to scale AI responsibly, optimize operations, and accelerate their journey toward autonomous cloud environments.”

- Satish Thomas, Vice President, Applied AI & Platform Ecosystem, Google Cloud

The rise of the AI workforce

Enterprises are rapidly shifting from localized experimentation to scaled AI adoption across their entire organization. However, operationalizing these advanced AI workloads introduces unpredictable costs and dynamic behavior that traditional monitoring tools simply can’t manage.

To safely deploy an AI workforce, teams require stringent observability, governance, and automation. Enter Dynatrace, offering AI-powered observability that tracks every input and output to help ensure compliance and reliable decision-making.

	The rise of the AI workforce: Enterprises need a new operating model – Blog
	The latest Dynatrace AI-powered observability features are now on Google Cloud – Blog

Powering autonomous cloud ops with real-time intelligence, context, and agentic AI guardrails

For agentic AI to function reliably, observability, real-time context, and deep intelligence are essential. Without these guardrails, organizations risk model drift, hallucinations, and degraded service quality.

Dynatrace directly supports event-driven preventive operations and AI-driven decisioning. By automatically discovering and continuously mapping all components in real time, Dynatrace Intelligence empowers teams to achieve safe, autonomous cloud operations.

	Enhance Healthcare Operations with Dynatrace & Google Cloud – Fact Sheet
	The pulse of Agentic AI in 2026 – Report
	Dynatrace Intelligence: Fuse deterministic and agentic AI for autonomous operations – Report

Accelerating cloud native innovation for modern cloud architectures

Dynatrace helps DevOps and platform engineers standardize, automate, and optimize complex cloud-native environments. By providing instant insights and proactive risk mitigation across Google Kubernetes Engine (GKE) and beyond, Dynatrace improves the developer experience and accelerates release cycles.

	Five real-world lessons for building developer workflows in the agentic era – Blog
	The top 7 Kubernetes challenges and how to solve them – Blog

Chart your course for cloud innovation

Dynatrace is collaborating with Google Cloud to help organizations scale AI responsibly, efficiently, and confidently. We aim to deliver AI-powered observability, automation, and precise analytics across your Google Cloud ecosystem. That’s why now is the time to optimize your Kubernetes clusters, automate your security pipelines, and deliver exceptional software.

The post Google Cloud Next 2026: A look into the autonomous future of cloud operations appeared first on Dynatrace news.

A profound shift is underway in enterprise software delivery.

Developers can now generate, modify, and deploy systems faster than ever using AI. But understanding what those systems are doing in production is getting harder, not easier.

What began as simple prompt and response interactions with LLMs has evolved into something far more powerful: a distributed system of humans and AI agents working together to build, run, and adapt software. This isn’t a theoretical future. It’s happening now, and it’s reshaping how organizations must architect, operate, and govern AI at scale.

The industry has moved beyond monolithic LLMs. According to Merlin Yamssi, AI/ML CoE Lead for Partner Engineering at Google, who spoke at Dynatrace Perform this year, the early era of “LLM + prompt” model broke down quickly in real systems: no context, inconsistent behavior, and no way to act safely. That drove a rapid evolution toward retrieval, tool use, and ultimately agents that can plan, act, and collaborate across systems.

Today, AI systems behave less like single models and more like teams: one agent retrieves context, another writes code, another validates changes, and another evaluated impact in production. This shift is redefining enterprise expectations. AI is no longer here just to respond. It’s here to work.

Three forces reshaping enterprise AI

Three major trends are accelerating this transformation.

1. Inputs are no longer just text, systems must interpret complex signals. As Yamssi put it, “You can show an image to a model, and then it will understand the image … and think on the image.”
2. Execution is no longer linear, systems coordinate across agents.
3. Data is no longer static, systems operate on constantly evolving context. “Essentially, [this turns] all the vast enterprise data into an active conversation,” Yamssi said.

Together, these forces are creating not just better models, but an entirely new AI operating model.

Why traditional AI architectures can’t keep up

Legacy architectures weren’t designed for distributed, autonomous AI systems. Single model approaches are rigid, difficult to debug, and prone to hallucinations. As organizations adopt multiagent systems, complexity skyrockets.

These are not only model problems; they are also distributed systems problems.

Emerging risks include the following:

• Agents lose context as they hand tasks to one another.
• Infinite loops occur where agents trigger each other endlessly, consuming tokens and budget.
• Opaque decision chains can make it difficult to understand why an agent acted.
• Token usage explodes without visibility or guardrails.

“You cannot really go into production with something that looks like a black box,” Yamssi added.

Enterprises should treat AI like a distributed system, not a chatbot.

What modern AI applications require

To support an AI workforce, organizations need a vertically integrated stack that spans five foundational layers: infrastructure, data, models, platform, and applications. Google Cloud is one of the few providers offering all five layers in a unified architecture, with hooks for observability at each layer.

On top of this foundation, a new class of agent specific tooling is emerging:

• ADKs for building reasoning capable agents
• MCP for standardized access to tools and enterprise systems
• A2A protocols enabling seamless agent-to-agent collaboration across environments
• Agent engines capable of running thousands of agents at scale

This is the new AI application stack, and it calls for a new operational model.

The missing layer: Observability for the AI workforce

Observability is increasingly about decisions, not just systems. As multiagent systems scale, observability can serve as a control plane.

Without deep visibility, enterprises face black box behavior, unpredictable costs, and operational risk. Dynatrace and Google Cloud are working to address this gap, including integrating observability capabilities with Gemini Enterprise, A2A, MCP, and other parts of the AI stack.

Modern AI observability should help reveal:

• How decisions are made
• How agents coordinate
• How costs and behavior evolve in real time
• Where failures originate across reasoning

This is a shift from monitoring applications to monitoring reasoning, decisions, and collaboration. Enterprises often need visibility from the infrastructure all the way to the data, the LLM, the agent, and the application.

For developers, this changes the job entirely. You’re no longer debugging a service. You’re debugging a system of agents, decisions, and interactions across code, cloud, and runtime behavior.

From insight to action: The path to autonomous operations

According to recent Dynatrace research, 50% of respondents have agentic AI projects in production for limited use cases, and 44% have projects in broad adoption. Further, 72% of respondents have 2-10 agentic AI projects. With agentic AI already in production and growing, observability becomes essential. Once organizations can observe their AI workforce, they can begin to automate operations.

Observability must surface not just what happened, but why. Dynatrace and Google Cloud are already enabling this through integrations with Gemini Cloud Assist, which can recommend infrastructure or application changes based on observed issues.

This unlocks a new operational loop:

• Observe systems behavior across code, runtime, and agents
• Diagnose causal relationships, not just symptoms
• Recommend changes grounded in runtime context
• Remediate automatically or in collaboration with developers and agents

The result can be faster recovery, lower cost, and safer AI deployment at scale.

Accelerate your AI workforce strategy with Dynatrace on Google Cloud

At Perform, this shift was clear: The challenge is no longer generating code or deploying models. It’s understanding and controlling how these systems behave once they’re running.

The organizations that solve this will define the next generation of software delivery. If you want to go deeper, watch the full Perform session: The AI workforce: Advancing agentic collaboration through observability.

Dynatrace and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies. All other trademarks are the property of their respective owners. © 2026 Dynatrace LLC

The post The rise of the AI workforce: Enterprises need a new operating model appeared first on Dynatrace news.

Multi-cloud architecture is now the norm. Each cloud brings unique strengths, and Alibaba Cloud often plays a key role for organizations with customers or operations in China and the broader Asia‑Pacific region. To make the most of the flexibility of multi-cloud environments, teams need a clear view of how their infrastructure fits together.

The Dynatrace Alibaba Cloud extension helps provide that visibility by bringing Alibaba Cloud telemetry directly into the Dynatrace AI-powered observability platform. The extension is provided to customers and supported by Dynatrace, and is developed by Phenisys, a French IT consulting firm and Premier Dynatrace® Sales Partner with more than 20 years of experience, supporting IT and application observability for customers operating complex, large‑scale environments.

Deep, integrated Alibaba Cloud visibility with Dynatrace

When operating at the scale of large multi-cloud deployments, unified observability becomes essential to avoid blind spots and fragmented operational workflows. The Alibaba Cloud Extension for Dynatrace allows you to observe and analyze Alibaba Cloud services alongside other cloud environments and application components, ensuring full-stack context and deep operational insights.

This extension is designed to:

• Automatically discover and monitor Alibaba Cloud components, with baseline behavior derived from observed metrics.
• Ingest rich metric streams for critical services such as Elastic Compute Service (ECS), Server Load Balancer (SLB), Network Address Translation (NAT) Gateway, Cloud Enterprise Network (CEN), Network Attached Storage (NAS), and VPN.
• Retrieve and correlate Alibaba tags with Dynatrace OneAgent entities, preserving native cloud metadata and enabling powerful filtering, grouping, and contextualization of telemetry.

How it works: Mechanisms and data characteristics

Unlike sampling-only tools or periodic polling models that provide limited context, the Dynatrace extension operates via remote ActiveGate instances that connect directly to Alibaba Cloud’s Cloud Monitor API endpoints.

Data collection model

• Pull vs poll: The extension pulls metrics directly from Alibaba Cloud’s monitoring APIs via ActiveGate. Dynatrace ingests metrics as exposed by those APIs, without introducing additional sampling beyond the source data.
• Granularity: Metrics such as CPU utilization, network throughput, IOPS, latency, and health statuses are reported at their native resolution (typically minute-level resolution with cloud provider API semantics).
• Topology and correlation: Resource identifiers and Alibaba tags are ingested so that ECS instances and other resources are correlated with the Dynatrace topology model and can be mapped to application performance and dependency graphs.

Licensing and data handling

• Metrics ingestion is metered based on actual metric data points. Dynatrace classic and platform subscription models calculate consumption accordingly.
• There is no upfront cost to activate the Alibaba Cloud extension in Dynatrace Hub. Charges apply only for the telemetry volume ingested.

AI-powered insights with Dynatrace

Dynatrace Intelligence is an agentic operations system at the core of the Dynatrace platform that fuses deterministic AI with agentic AI to drive a new level of reliability across observability and AI-powered operations. By unifying Alibaba Cloud telemetry with broader application and infrastructure data, Dynatrace customers can:

• Detect performance deviations across services.
• Evaluate cross-layer dependencies automatically.
• Provide causal analysis to accelerate resolution during incidents.

This level of intelligence helps reduce alert fatigue and empowers teams to identify underlying issues and rapidly take remediation steps—even in complex multi-cloud environments where manual correlation would be prohibitively time-consuming.

Business and operational benefits

Bringing Alibaba Cloud into the Dynatrace observability fold enables:

• Unified dashboards and governance across hybrid and multi-cloud stacks.
• Faster troubleshooting with contextualized telemetry and AI-powered analysis.
• Metadata-driven insights using native Alibaba tags correlated with Dynatrace topology.
• Support improved operational efficiency by eliminating blind spots and integrating cloud metrics with service performance and user experience.

Getting Started

For IT leaders, Ops, or SRE teams, the Alibaba Cloud extension is accessible via the Dynatrace Hub and fully documented for deployment.

Further reading

• Get the extension from the Hub:
  • Alibaba Cloud Extension
• Technical documentation:
  • Alibaba Cloud Extension Documentation page
• Other apps built by Phenisys:
  • MS Teams App

Dynatrace®; OneAgent®; ActiveGate®; and Dynatrace Intelligence are trademarks of the Dynatrace, Inc. group of companies. All other trademarks are the property of their respective owners.

The post Achieving enhanced observability for Alibaba Cloud in multi-cloud environments with Dynatrace appeared first on Dynatrace news.

Editor’s note

The explosion of applications, data, and cloud-native workloads has created an IT landscape that’s harder than ever to manage. Organizations face overwhelming complexity, fragmented visibility, and mounting pressure to innovate while maintaining uptime and controlling costs.

Nutanix .NEXT 2026 addresses these challenges head-on. Taking place April 7-9 in Chicago, this three-day event brings together IT leaders, innovators, and practitioners who are solving the same problems you face: managing hybrid multicloud environments, optimizing AI workloads, and maintaining observability at scale.

The future demands infrastructure that can run anything, anywhere, paired with intelligent observability that transforms complexity into clarity. By combining Nutanix’s unified hybrid multicloud platform with Dynatrace AI-powered observability, organizations gain the foundation needed to innovate faster, operate more efficiently, and deliver exceptional digital experiences at scale.

This guide highlights the key themes of Nutanix .NEXT 2026, which Dynatrace is proud to sponsor, and explores how we partner to help organizations build, modernize, and secure their environments with confidence.

— Jay Snyder, SVP of Global Partners and Alliances, Dynatrace

The latest news and announcements from Nutanix .NEXT

As Nutanix .NEXT 2026 kicks off, this year’s announcements and integrations highlight how Dynatrace and Nutanix are shaping the future of hybrid multicloud and enterprise AI.

With Dynatrace Intelligence, which brings together deterministic and agentic AI to help teams manage complexity and AI-powered environments with reliable automation, and Nutanix’s unified hybrid multicloud platform, organizations can finally break through the complexity created by sprawling applications, data, and cloud-native workloads. By combining Nutanix’s resilient, scalable infrastructure with Dynatrace’s end-to-end visibility and automated root cause analysis, the partnership delivers a single, intelligent platform that improves reliability, accelerates innovation, and elevates digital experiences.

Check out the latest news and integrations:

• Dynatrace has been named the Global Innovation Partner of the Year at Nutanix .NEXT. This prestigious recognition highlights our exceptional collaboration with Nutanix over the past year, particularly our innovations in supporting the Nutanix Kubernetes Platform (NKP) and Nutanix AI (NAI).

	Nutanix AI – Technology End-to-end observability for your Nutanix AI agentic and LLM workloads.
	Nutanix AHV – Technology Monitor Nutanix AHV virtual machines from the guest OS perspective.
	Nutanix Clusters – Extension Monitor Nutanix clusters’ performance, usage and availability, with Nutanix API.
	Nutanix Kubernetes Platform (NKP) – Technology All-in-one Kubernetes observability for K8s infrastructure and apps teams.

Deploy AI responsibly and at speed with unified insight and optimized performance

AI is transforming how organizations operate, but building trust in these systems remains a work in progress. According to the recent Dynatrace Pulse of Agentic AI report, trust and lack of observability are top technical barriers to production. This highlights the critical need for unified observability that provides visibility into AI workloads from infrastructure to user interactions.

Nutanix provides specialized infrastructure optimized for AI workloads, including large language models (LLMs), with performance optimization, cost management, and scalability. Dynatrace adds comprehensive AI and LLM observability, enabling teams to monitor, debug, optimize, and audit agentic workflows. Together, Dynatrace and Nutanix help organizations operationalize AI faster while maintaining control and transparency.

	Dynatrace introduces a new foundation for agentic AI at Perform 2026 – Blog Learn how we’re expanding our platform to carry organizations into the human + AI collaboration era.
	Building trust in agentic AI: An observability‑led 90‑day action plan – Blog New research outlines a 90‑day plan to scale agentic AI with governance, human oversight, and observability as a real‑time control plane.
	Dynatrace agentic ecosystem: Drive real outcomes, not AI pilots – Blog Dynatrace has evolved into an agentic operations system that provides autonomous collaboration across dev, ops, and business workflows.
	Shaping the Future: Autonomous Intelligence by Dynatrace – Blog Agentic AI has high potential but requires a solid foundation. CTO Bernd Greifeneder breaks it down in his executive blog.

Get unified hybrid cloud visibility through a single platform for apps, data, and observability

Organizations face growing blind spots as applications and data spread across physical, on-premises, and cloud environments. Fragmented monitoring tools create disconnected views of the hybrid landscape, making it difficult to understand system health, anticipate issues, or optimize resources.

Nutanix provides one platform to run all applications and data anywhere—from on-premises to public cloud and edge locations—establishing a consistent operational foundation. Dynatrace adds a unified observability platform that delivers real-time, AI-powered insights across the entire hybrid stack, eliminating tool sprawl and stitching together context across infrastructure, applications, and user experience.

Together, Nutanix and Dynatrace give teams the visibility and intelligence needed to:

• Strengthen cloud and application resilience with continuous, full-stack insights that detect anomalies early and maintain performance across hybrid environments.
• Enable event-driven, preventive operations through Dynatrace Intelligence, which automatically correlates signals, identifies root causes, and triggers remediation workflows before issues affect users.
• Optimize architecture and resource utilization by connecting Nutanix infrastructure metrics with application and business performance data, helping teams right-size resources, reduce waste, and improve efficiency.

This unified approach breaks down silos, reduces operational complexity, and empowers organizations to move from reactive firefighting to proactive, intelligent cloud operations.

	Cut costs and complexity: 5 strategies for reducing tool sprawl with Dynatrace – Blog Tool sprawl can increase costs, inefficiencies, and risk. Executives can avoid these pitfalls with these five tips from Bernd Greifeneder.
	Powering consolidation on a unified platform – Webinar Join our three-part webinar series to explore how modern businesses can simplify, scale, and succeed like never before.
	5 considerations when deciding on an enterprise-wide observability strategy – Blog Discover the five most important things to consider before settling on an enterprise-wide unified observability strategy.

Take the next step in your transformation

Discover how Dynatrace and Nutanix can help you automate complex tasks, optimize your hybrid multicloud environment, and accelerate the adoption of enterprise AI technologies. Check out Dynatrace Principal Solution Architect Rob Jahn’s expo speaking session, “Observability built for the age of AI: Autonomous operations,” to learn how Dynatrace’s agentic AI foundation, unified topology model, and end-to-end observability power a modern operating model on Nutanix.

And be sure to visit Dynatrace at booth S8 to see live demos, connect with our experts, and explore how our AI-powered observability platform integrates seamlessly with Nutanix infrastructure.

The post Nutanix .NEXT 2026: Turning cloud complexity into clarity with AI-powered observability appeared first on Dynatrace news.

Dynatrace has surpassed $1 billion in lifetime AWS Marketplace sales and earned the AWS Financial Services Competency—a milestone that reflects expanded co-innovation, deepened AI-powered observability capabilities, sustained triple-digit growth over the past three years, and accelerated customer adoption across global markets.

These achievements build on the advancements announced at AWS re:Invent 2025, including attaining the AWS Agentic AI Specialization, expanding integrations with Amazon Bedrock AgentCore and AWS DevOps Agent, and being named AWS Public Sector Technology Partner of the Year for LATAM.

Here is a closer look at how these milestones advance agentic AI observability and cloud operations for customers on a global scale.

The power of a strategic collaboration that puts customers first

Reaching $1 billion in sales on AWS Marketplace is a testament to the powerful synergy between Dynatrace and AWS. Dynatrace growth in AWS Marketplace sales has surged over the past three years, delivering sustained triple-digit growth.

As enterprises adopt cloud-native procurement to accelerate modernization, demand for Dynatrace continues to expand globally. AWS Marketplace has been instrumental in supporting this growth, enabling Dynatrace to reach customers across industries in over 20 regions through streamlined tax handling, multi-currency support, and other strategic offerings.

“Collaborating with Dynatrace through AWS Marketplace has been transformative for our joint customers,” said Ed Smoke, Vice President of Intelligent Operations Partner Alliances at AHEAD. “The streamlined procurement process and seamless integration of the Dynatrace AI‑powered observability platform with AWS services enable organizations to accelerate modernization with confidence. By leveraging AWS Marketplace, we’ve helped customers align their investments with AWS Enterprise Discount Programs, delivering greater value and efficiency.”

Global scale meets local impact

Enterprises continue to accelerate Dynatrace adoption through AWS Marketplace to streamline procurement, reduce onboarding time, and align spending with AWS Private Pricing Addendum. Customers trust the Dynatrace platform to:

• Simplify procurement, streamlining the buying process to get technology into the hands of teams faster.
• Optimize cloud spend, utilizing AWS committed spend to invest in observability that drives efficiency.
• Scale confidently, deploying Dynatrace across complex, multi-region AWS environments with ease.

These organizations aren’t just buying software; they are investing in a platform that serves as the foundation for their digital resilience. And this streamlined route allows customers to adopt the Dynatrace AI-powered observability platform quickly while maximizing the value of their AWS investments.

“Partnering with Dynatrace through AWS Marketplace has been a strategic win for Storio group,” said Alex Hibbitt, Engineering Director, Customer Platform at Storio group. “The streamlined procurement process, providing efficiency for both our teams and our vendors, has allowed us to quickly adopt the Dynatrace AI-powered observability platform to gain real-time insights across our cloud environments. By centralizing our purchasing through AWS Marketplace, we were able to align our spend with our commitment to AWS, maximizing the value of our cloud investments while accelerating our modernization journey. The Dynatrace platform’s seamless integration with AWS services has empowered us to innovate faster, reduce operational complexity, and focus on delivering exceptional value to our customers.”

Agentic AI is fueling the next wave of innovation

While generative AI remains wildly popular, agentic AI—systems that don’t just generate content but take action—is moving to the fore. In fact, according to the recent Dynatrace research report, The Pulse of Agentic AI, 50% of agentic AI projects are in production for limited uses or departments, and 23% are in mature, enterprise-wide integration. Further, 72% of respondents expect agentic AI budgets to increase in the next year.

Dynatrace recently earned the AWS Agentic AI Specialization—a distinction that validates our deep technical expertise in observing and governing agentic AI systems.
As organizations move from AI experimentation to production, they face new challenges, including how to monitor an AI agent that acts autonomously and how to ensure it stays within its guardrails.

Our expanded collaboration with AWS directly addresses these needs through integrations designed to provide end-to-end visibility into an AI ecosystem.

1. Amazon Bedrock AgentCore Observability

Dynatrace continues to expand deep technical integrations with AWS to support modern cloud‑native and AI‑driven architectures. Dynatrace provides full‑stack analytics across services such as Amazon Bedrock AgentCore and AWS automation pipelines, enabling teams to operate, secure, and scale agentic AI workloads with confidence.

To build agents on Amazon Bedrock, teams need more than just logs—they need context. This new integration provides native, end-to-end observability for Amazon Bedrock AgentCore, enabling developers and site reliability engineers to:

• Monitor agent interactions across various AWS offerings.
• Debug complex workflows by tracing requests from the user to the LLM and back.
• Audit performance to ensure agents deliver accurate, safe, and efficient results.

2. Kiro powers and Kiro Autonomous Agent

Dynatrace is also integrating with Kiro, AWS’s agentic integrated development environment. Kiro leverages deep insights from the Dynatrace AI-powered observability platform to accelerate developer productivity.

Imagine an AI agent that can handle bug triage, suggest code fixes, or even implement features—all while being guided by the precise telemetry data from Dynatrace. This Kiro Powers integration extends observability directly into the developer workflow, enabling:

• Faster root-cause analysis. Agents can autonomously troubleshoot issues based on real-time performance data.
• Spec-driven development. Actionable insights help developers build higher-quality code from the start.

3. AWS DevOps Agent

To further streamline operations, Dynatrace has integrated with the AWS DevOps Agent. This collaboration accelerates root-cause isolation by adding domain-specific AWS context to Dynatrace findings.

The result is autonomous troubleshooting that detects performance degradations, quantifies their business impact, and provides clear remediation instructions. It’s about reducing the noise to let teams focus on solving the problems that matter most.

“By leveraging AWS Marketplace, we were able to align our spend with our AWS Enterprise Discount Program, maximizing the value of our cloud investments while accelerating our modernization journey.” said Luca Domenella from Soldo.

Accelerate your cloud journey with Dynatrace and AWS

Reaching $1 billion on AWS Marketplace is a historic moment for Dynatrace, but it’s just the beginning. The combination of triple-digit growth in AWS Marketplace sales over the past three years, expanded deal sizes, and groundbreaking innovation in agentic AI, Dynatrace is moving faster than ever.

Whether you are looking to simplify your cloud operations, secure your AI workloads, or simply get more value from your AWS investment, Dynatrace is the partner you need. Check out our AWS Marketplace listing to see how easy it is to get started, and contact us today for more information.

The post Dynatrace accelerates a new era of growth and innovation with AWS appeared first on Dynatrace news.

You know that moment when everything should be working, but it’s just not? Your deployment went smoothly, the pods are running, and yet — errors. Lots of them.

That’s exactly where I found myself recently. The EasyTrade application was throwing fits, and I needed answers fast. What made this debugging session different, however, was the combination of tools at my disposal: Dynatrace intelligent monitoring paired with the raw power of Gemini CLI accessing the Dynatrace MCP server.

The fact that Gemini CLI can communicate directly with Dynatrace through the MCP protocol means I can stay in my flow. I’m not jumping between tools, losing my train of thought, or rebuilding mental context every few minutes. I ask questions in natural language, and Gemini translates that into precise DQL queries against Grail.

Let’s walk through two tales from the terminal: one about an impatient service, and another about a phantom error that taught me patience.

The impatient service that couldn’t wait

When detection meets investigation

I started my day firing up my email and my terminal. Dynatrace flagged the first problem before I’d even finished my coffee. A notification in my inbox let me know the contentcreator service was experiencing a stuck deployment.

Now, here’s where things get interesting. Dynatrace identified what was broken, but I wanted the gritty details and potential solutions. This is where Gemini CLI comes in clutch.

Instead of clicking through UI panels — nothing wrong with that, by the way — I fired up my terminal and started a conversation with Dynatrace via the Gemini CLI. Think of it as having a debugging buddy who speaks fluent Dynatrace Query Language (DQL) and can pull data from Dynatrace Grail® faster than you can say observability.

As you can see, I asked Gemini to tell me more about the problem. The results were crystal clear. Every log entry showed the same error:

"com.microsoft.sqlserver.jdbc.SQLServerException: The TCP/IP connection to the host db, port 1433 has failed. Error: "Connection refused. Verify the connection properties. Make sure that an instance of SQL Server is running on the host and accepting TCP/IP connections at the port. Make sure that TCP connections to the port are not blocked by a firewall."

The race nobody wins

Here’s the thing about microservices: They’re like orchestra musicians warming up. Everyone’s eager to start playing, but if the conductor — in this case, the database — isn’t ready, you get chaos instead of symphony.

The contentcreator service was starting up and immediately trying to connect to the database. However, SQL Server inside the db pod needed a few extra seconds to initialize. Classic startup race condition. The contentcreator service was essentially knocking on a door that hadn’t fully opened yet.

The fix? Kubernetes startup probes. These probes are brilliant; they’re like a bouncer at a club, making sure nobody rushes in before the venue is ready.

Gemini CLI modified the db service deployment manifest to include a readiness probe that would execute a simple SQL query. The container wouldn’t be marked as “running” until it could successfully respond to that query. Simple, elegant, effective.

I went from error to root cause to the recommended solution to fix, all without leaving the terminal.

After applying the updated manifest and restarting the pods, I turned back to Gemini CLI to run the same log query against the fresh manager pod. Just like that, the database connection errors were gone.

The phantom error and the deployment maze

New problem, different beast

Just when I thought I could relax, Dynatrace surfaced another issue. This time, it was the credit-card-order-service showing a failure rate increase. Different service, different problem. Same reliable detection system.

Back to Gemini CLI.

I pulled up a list of current problems. Gemini knew the resource contention issues we were seeing were expected, but Dynatrace flagged something else — a problem with multiple services impacting 98 users. That one needed our attention.

Gemini was able to immediately determine that the issues were caused by a feature flag and recommended we turn that feature flag off and even updated and applied the manifest for us. Easy peasy.

After deploying the updated feature flag, I remained in Gemini CLI to verify the fix. The logs were clean — no more ArithmeticException. Within minutes, Dynatrace had correlated the fix across all affected services and automatically closed the problem. Once again, the whole workflow — from detection, to investigation, to fix, to verification — happened without me leaving my terminal.

A single workflow in a single place

The synergy between Dynatrace and Gemini CLI isn’t just about having two good tools. It’s about having the right tool at the right time in the right place.

Dynatrace excels at the big picture. It detects anomalies, correlates issues across your entire application landscape, and surfaces problems you didn’t even know existed. It’s your early warning system.

Gemini CLI, connected through the Dynatrace MCP server, lets you query Dynatrace without leaving your terminal. Look, the web UI can do all of these same things and then some — dig into logs, build DQL queries, verify fixes, show pretty dashboards. But when you’re already neck-deep in terminal work, staying put is the move. No alt-tabbing. No mental context switching. Just you, your terminal, and your data.

And here’s where it gets even better: Gemini CLI isn’t just passive. It’s agentic. It suggests fixes, modifies manifests, and can even deploy changes on your behalf. You saw this in action with both problems we tackled: Gemini recommended the readiness probe solution and updated the deployment manifest. It identified the feature flag issue and applied the fix.

The takeaway

Both of these troubleshooting sessions taught me something valuable. The startup race condition showed me the importance of proper initialization ordering in Kubernetes.

But the biggest lesson was to trust your tools but understand what they’re doing. When Dynatrace takes a few minutes to close a problem after you’ve verified the fix, it’s not lagging — it’s being thorough. It’s analyzing patterns, checking for recurrence, and making sure you can confidently move on to your next task.

The combination of intelligent monitoring and command-line power creates something greater than the sum of its parts. Dynatrace watches everything, spots the problems, and shows root cause. Gemini CLI lets you investigate and address those problems without breaking your flow. Together, they make troubleshooting feel less like detective work and more like having a conversation with your infrastructure.

And that morning cup of coffee? It actually stayed warm this time.

The post When your service won’t wait: Troubleshooting EasyTrade with Dynatrace MCP and Gemini CLI appeared first on Dynatrace news.

Editor’s note

At AWS re:Invent, innovation isn’t just a topic of conversation — it’s on full display for the world to see. As we prepare for the latest innovations from the expo floor in Las Vegas, the message is clear: The future is AI-driven, and the foundation for success is built on intelligent observability.

This presents a unique opportunity, and Dynatrace with Amazon Web Services provides a clear path forward. With AI-driven automation and real-time observability at the core, we help organizations reduce risk, resolve issues faster, and optimize cloud investments.

This guide explores the key themes of AWS re:Invent and highlights how Dynatrace and AWS empower organizations to build, modernize, and secure their cloud environments with confidence. It’s time to accelerate and lead the way.

— Jay Snyder, SVP of Global Partners and Alliances, Dynatrace

The latest news and announcements from AWS re:Invent

The Dynatrace AI-powered observability platform is purpose-built to tame the complexity introduced by new generative and agentic AI initiatives and the explosion of data that teams must manage. By integrating with new agentic-focused services from Amazon Web Services, Dynatrace provides automated, intelligent observability and security that organizations need to innovate faster and more securely. Check out the latest news and integrations, including the following:

• AWS DevOps Agent — an autonomous AI agent that resolves and proactively prevents incidents, while continuously improving reliability and performance — has reached general availability. Dynatrace has collaborated with AWS on this initiative from the beginning. AWS DevOps Agent works with Dynatrace production context to reduce operational toil, identify recurring issues, and strengthen application reliability across AWS environments.
• Our new Modern Cloud Operations for AWS feature enables automatic discovery of new AWS services, native telemetry and metadata ingestion for seamless observability, and unified dashboards with AI-driven insights for performance, cost control, and modernization.
• Dynatrace is now integrated with Kiro, AWS’ agentic integrated development environment.
• Customers leveraging agentic systems built on AWS services like Bedrock AgentCore can get visibility into their interactions across AWS services, enabling developers to monitor, debug, optimize, and audit agentic workflows with Dynatrace’s support for Amazon Bedrock AgentCore Observability.
• Teams can perform cloud security posture reviews and receive real-time observability and AI-driven insights, accelerating threat detection, reducing MTTR, and improving resilience and compliance via our Dynatrace and AWS Security Hub integration.

For more information on these integrations and the latest news:

	Announcing Amazon Bedrock AgentCore Agent Observability – Product News Dynatrace now provides native, end-to-end observability for Amazon Bedrock AgentCore agents.
	Leverage Dynatrace observability capabilities within Kiro powered by AWS – blog By integrating Kiro powered by AWS with Dynatrace, you can leverage AI-assisted monitoring and troubleshooting directly in your development workflow.
	Dynatrace Expands AWS Integrations at re:Invent 2025 – Product News Dynatrace announced expanded integrations with advanced AWS technologies and new achievements with AWS that deliver enhanced AI-driven observability, automation, and security to customers worldwide.

How Dynatrace and AWS help navigate the complexities of agentic and GenAI

Generative and agentic AI are transforming industries. However, building trust in these systems is a work in progress. In fact, according to the Dynatrace 2025 State of Observability report, 99% of AI governance leaders report their organization takes human-monitored measures to validate AI decision-making, highlighting the need for unified observability. See how Dynatrace and AWS help organizations overcome the complexities that modern AI workloads introduce.

	How Dynatrace drives value in the age of AI in the AWS® Agentic AI Marketplace – blog Agentic applications are transforming business. Discover how to operationalize AI fast with Dynatrace and the AWS Agentic AI Marketplace.
	The rise of agentic AI part 3: Amazon Bedrock Agents monitoring and how observability optimizes AI agents at scale – blog Next-level agentic AI relies on A2A communication. Discover how to optimize AI agent observability and Amazon Bedrock Agents monitoring.
	Dynatrace achieves AWS Generative AI Competency: A new milestone in observability and AI – blog Dynatrace has achieved the AWS Generative AI Competency to help organizations maximize the benefit and full potential of GenAI projects.
	Exploring the power of AI observability with Dynatrace and AWS – webinar Unpack today’s AI observability friction points and learn how AWS and Dynatrace help companies run AI with confidence.
	Unlock innovation with AI-powered observability from Dynatrace for Amazon Bedrock – fact sheet Take control of your generative AI systems with Dynatrace’s observability solutions. Gain insights, optimize performance, and build trust across your AI stack—from infrastructure to user interactions.

Discover the keys to smarter, safer innovation — faster

Constant firefighting can be a time drain for developers, site reliability engineers, security, and operations teams. Instead, they need to spend more time on business-critical tasks — most notably, innovation. That’s where Dynatrace and AWS can help, offering a strategic approach to optimizing applications for performance, cost, and security. Dynatrace and AWS provide end-to-end observability and AI-powered insights that reduce risk and accelerate modernization. From managing cloud complexity to protecting critical data and accelerating AI-driven innovation, Dynatrace on AWS provides the tools and insights teams need to succeed.

	Enhance your development workflow with the Amazon Q Developer CLI for Dynatrace MCP – blog Enhance your development workflow by integrating Amazon Q Developer CLI with the Dynatrace AI-powered observability platform using MCP.
	AWS: Driving successful cloud migration and optimization with Dynatrace – video Hear how AWS is utilizing Dynatrace to help enable customers to provide a path to make intelligent decisions and drive better business outcomes.
	Ingest and enrich AWS Security Hub findings with Dynatrace – blog Dynatrace integrates with AWS Security Hub to unify, visualize, and automate security findings across tools and environments.
	Ingest and enrich Amazon GuardDuty security findings with Dynatrace – blog This integration empowers SREs and security teams to understand runtime context for smarter threat detection, faster issue remediation, and more.
	AWS publishes Dynatrace-developed blueprint for secure Amazon Bedrock access at scale – blog Organizations can now securely and efficiently control access to Amazon Bedrock services at scale.
	Smarter cloud security with Dynatrace and Kiro CLI – blog We’ve integrated Dynatrace with AWS Security Hub and Kiro CLI to streamline triaging and remediation of critical findings, focusing efforts where they count most.

Chart your course for innovation

AWS re:Invent is more than a conference; it’s a catalyst for the next wave of technological advancement. Agentic AI is poised to redefine what’s possible, and the powerful synergy between Dynatrace and AWS provides the foundation you need to lead the way.

Visit us at booth #575 to see live demos, chat with our experts, and explore how Dynatrace can help you automate complex tasks and optimize your AWS ecosystem while accelerating adoption of generative and agentic AI technologies. Don’t miss our meetups, breakout sessions, and lightning talks to gain deeper insights and network with fellow innovators.

The post AWS re:Invent 2025: Accelerate into the age of agentic with AI-powered observability appeared first on Dynatrace news.

For many teams, the path to automation starts with connecting data and workflows across platforms. Dynatrace and ServiceNow make that possible. Through a growing set of integrations, customers can seamlessly connect both platforms to create incidents, enrich CMDB data, and leverage AI-driven insights for faster, smarter decisions.

Six ways to link Dynatrace data with ServiceNow workflows

Our first six integrations are now available in the ServiceNow store. Each one enriches context and helps you remediate issues faster than ever.

Advance your IT operations with Dynatrace and ServiceNow: Four steps to autonomous remediation

Every organization’s automation journey looks different, but the path typically unfolds in stages. Each step builds new capabilities and confidence in automation.

1. Context-rich incident creation

Start your journey by connecting Dynatrace® to ServiceNow ITSM to automatically create incidents with context enrichment and root-cause description to allow for uniform tracking and resolution of tickets. Dynatrace offers two methods for ticket creation within ServiceNow:

• Dynatrace Incident Integration Application
• Dynatrace Workflows for ServiceNow

Both methods can generate a ticket and populate the incident with Dynatrace-available context, such as:

• Root-cause analysis identifying the exact component causing issues
• Correlation identifiers for all affected hosts and infrastructure
• Business impact assessment showing affected services and users
• Dependency context explaining how the problem propagates

For advanced customization, Dynatrace Workflows for ServiceNow offers more flexibility than the standard Incident Integration Application, which is triggered based on Dynatrace-identified problems. This allows customers to further enrich incident descriptions, add additional information such as logs or metrics, or leverage predictive analytics to initiate remediation procedures even before a problem occurs.

This step helps teams eliminate manual triage and ensures every incident includes complete diagnostic context.

2. Real-time configuration management database (CMDB) enrichment

Once your organization is creating incidents, the next step is to add enrichment methods such as associated configuration items for impact analysis and dependency information.

Most organizations’ CMDB management is maintained manually and updated weekly at best. Connecting Dynatrace Smartscape® topology mapping to ServiceNow’s CMDB through ServiceNow Service Graph Connector (SGC) allows real-time entity identification, topology, and dependency mapping—all of which can be leveraged for automatic CI binding. This creates a living representation of an organization’s IT environment that updates continuously as the infrastructure evolves and helps teams make fast, informed decisions. The Service Graph Connector provides:

• Automatic population of ServiceNow CMDB with entity information and real-time topology
• Visibility of all dependencies across a cloud stack and Kubernetes
• Service Mapping tree generation showing the holistic impact of incidents and events
• Continuous synchronization as the environment changes

The result is a continuously accurate service map that evolves as fast as your environment.

3. Unified event management

To drive uniformity across your organization and reduce alert fatigue, organizations look to leverage ServiceNow as a central event management system with AIOps features enabled. By sending events to ServiceNow ITOM, teams can aggregate and prioritize alerts using AI-driven analytics, ensuring consistent visibility across platforms.

This provides organizations with a unified dashboard that embeds Dynatrace charts and metrics directly into ServiceNow dashboards, removing administrative silos for viewing information across platforms.

Once events and incidents are unified, the next step is intelligent assistance.

AI-powered assistance

As automation evolves, AI agents bridge the gap between insight and action. Once your organization has Dynatrace events flowing to ServiceNow ITOM, you can connect ServiceNow Now Assist with Dynatrace to allow administrators to request information from the Dynatrace console, such as recommended remediation actions or further details on root cause.

The Dynatrace AI Analysis Agent accelerates investigation by gathering key diagnostic details automatically.

4. Autonomous remediation

The journey toward autonomous operations culminates in closed-loop remediation, where systems detect, act, and validate automatically. Mature organizations rely on self-healing workflows to reduce manual effort and accelerate recovery. Whether triggered from Dynatrace or ServiceNow, each workflow can include validation steps that query Dynatrace APIs to confirm the problem is truly resolved, rather than just masked. Example workflows include the following:

• Restart services when memory saturation is detected on application servers.
• Scale infrastructure when capacity constraints are predicted.
• Rollback deployments when anomalies correlate with recent code changes.
• Clear caches when response time degradation is linked to cache performance.
• Reset database connections when connection pool exhaustion is identified.
• Create context-rich alerts or incidents for faster triaging (no remediation without incident).

Unlocking the future of autonomous IT operations

ServiceNow and Dynatrace together form a powerful foundation for intelligent, scalable, and future-ready IT operations. By combining real-time context with AI-driven insights, organizations can reduce noise, resolve issues faster, and operate more efficiently—around the clock. As agentic integrations mature, they pave the way for autonomous collaboration, delivering immediate value while laying the groundwork for a resilient, AI-powered future.

The post Accelerate your autonomous IT operations journey with Dynatrace and ServiceNow integrations appeared first on Dynatrace news.

Editor’s Note

Microsoft Ignite represents more than just another industry conference. It’s where the future of enterprise technology takes shape. This year’s event showcases the intersection of cloud innovation and artificial intelligence — and the strategic partnerships making that transformation possible.

Success in the cloud during the AI era requires visibility, automation, and intelligence working together seamlessly to turn growing complexity and risk into your new competitive advantage.

At Microsoft Ignite, Dynatrace and Microsoft will demonstrate on the expo floor and in hands-on demos how integrated, AI-powered observability works and can be a part of your systems today.

This guide captures the key themes that define the Microsoft and Dynatrace partnership — and how Dynatrace is helping enterprises move faster, operate smarter, and innovate with confidence across their Microsoft environments.

— Jay Snyder, SVP of Global Partners and Alliances, Dynatrace

The latest news and announcements from Microsoft Ignite

Get the latest announcements straight from the showroom floor for the latest integrations and innovations from Dynatrace and Microsoft.

• Dynatrace and Microsoft Partner to Scale Enterprise Customer AI Initiatives
  New integration enables customers to automate majority of cloud operations tasks
• Dynatrace Announces New Cloud Operations Solution for Microsoft Azure
  Provides customers with comprehensive visibility into their Azure environments, enabling automatic remediation, prevention, and optimization
• Dynatrace launches first GitHub custom agent, consolidating observability for developers
  Dynatrace launched its first custom agent, introducing a powerful new entry point for Dynatrace insights directly within GitHub repositories.
• Dynatrace and Azure SRE Agent unite for autonomous operations
  The integration of Dynatrace with Microsoft Azure SRE Agent establishes a new benchmark for cloud operations.

How Dynatrace and Microsoft work together to drive customer success

Modern cloud environments and agentic workflows need more than dashboards. Dynatrace on Azure provides real-time insight, automated answers, and end-to-end visibility with one-click deployment. Additionally, teams can unify, visualize, and automate security findings across their tools and environments to keep cyber threats at bay. See how we protect your critical data, simplify complexity, and drive innovation — with AI-powered observability right inside Microsoft Azure.

	Ingest and enrich Microsoft Sentinel security alerts with Dynatrace – Product News Dynatrace integrates with Microsoft Sentinel to unify, visualize, and automate security findings across tools and environments.
	Ingest and enrich Microsoft Defender for cloud findings with Dynatrace – Product News Dynatrace integrates with Microsoft Defender for Cloud to centralize security findings from across an organization’s tools and environments.
	Transforming Azure Data Factory operations with Dynatrace – Blog Data engineers who deploy Dynatrace with ADF gain valuable insights into performance, business analytics, and automation.
	Ingest and enrich GitHub Advanced Security vulnerability findings with Dynatrace – Product News Dynatrace integrates with GitHub Advanced Security to break down the silos between DevSecOps teams, unifying security findings along the SDLC and enriching them with runtime context.

Power your AI initiatives in the cloud

AI workloads introduce unique observability requirements. Discover how Dynatrace, an Azure native service, provides end-to-end observability for generative AI, agentic systems, and LLM services to enable smarter operations.

	The future of AIOps: How agentic AI is transforming IT resilience – Webinar Explore how AIOps is evolving—and what it means for your teams, tools, and transformation strategy.
	Analyze, automate, and innovate faster with AI-powered observability – Ebook Learn how the partnership between Accenture, Dynatrace, and Microsoft helps you cut downtime, enhance performance, and speed up innovation.

Join us at booth 5438 to experience our platform in action and connect with experts who understand the unique challenges of modern cloud environments. And don’t forget to attend our demo theater presentation on Wednesday, Nov. 19 at 11:30 a.m PST. to learn how FreedomPay uses Dynatrace to gain a holistic view of transactions, reduce resolution times by 80%, scale across AI workloads, and optimize performance to drive innovation.

The post Microsoft Ignite 2025: Turn complexity into a strategic asset in the age of AI appeared first on Dynatrace news.

Key insights:

• Why it matters. Traditional IT operations are caught in a reactive cycle of alerts, tickets, and manual fixes that slow innovation, drive up costs, and drain resources.
• What’s new. The Dynatrace and ServiceNow partnership introduces a new model for autonomous IT operations that connects observability and automation to predict and resolve issues before they impact users.
• How it works. Dynatrace provides real-time, causal intelligence through deterministic and agentic AI, while ServiceNow transforms those insights into automated, closed-loop workflows that detect, diagnose, and remediate incidents.
• Proof in action. Organizations such as BT Digital, CareSource, and Commerzbank have already reduced incident volume and mean time to resolution (MTTR), demonstrating measurable gains in reliability and efficiency.
• The bigger impact. Together, Dynatrace and ServiceNow are enabling self-healing IT ecosystems that elevate operations from reactive firefighting to proactive, continuous optimization across multicloud environments.

—

It’s 3 a.m., and a critical service alert lights up your phone. You log in, sift through dashboards, trace dependencies, and chase symptoms while customers wait. By the time the root cause is found, you’ve lost hours, sleep, and user trust.

Traditional IT operations are stuck in a loop of alerts, tickets, and manual fixes—always responding, never advancing. That approach is no longer sustainable. As systems expand across clouds and teams, the cracks widen. When incidents strike, teams scramble to manually correlate data across disconnected systems to pinpoint root cause and impact, wasting precious time, as outages drag on.

A new model is taking shape, shifting IT from reacting to predicting.

The timeline for autonomous IT operations accelerates today with a new, multiyear partnership between Dynatrace and ServiceNow. Together, our goal is to move enterprises from managing incidents to managing intelligence: creating a foundation for systems that see, decide, and act on their own.

“We help customers anticipate issues, coordinate remediation, and continuously optimize services by combining deterministic and agentic AI — bringing them closer to autonomous prevention, remediation, and optimization at enterprise scale.”
— Steve Tack, chief product officer at Dynatrace

Why reactive operations can’t keep up

Most organizations still operate in reactive mode. The symptoms are familiar:

• Visibility gaps fragment understanding across applications, infrastructure, and cloud environments.
• Manual incident response requires human intervention at every step, from detection through resolution. Problems are often discovered only after users are affected.
• Alert fatigue overwhelms teams with noise instead of insight. Without business context, it’s nearly impossible to prioritize what truly matters.

The result: slow mean time to resolution (MTTR), recurring issues, and IT teams trapped in a costly cycle of firefighting instead of innovation.

Connecting observability and automation through AI

Breaking the cycle requires more than adding automation to existing processes. It requires aligning observability and automation so systems can see clearly, decide confidently, and act autonomously.

Dynatrace and ServiceNow are advancing toward agentic operations, where AI agents collaborate across platforms to detect, triage, and resolve issues end-to-end. These systems share context, coordinate decisions, and execute actions autonomously, creating a continuous flow between observability and automation.

This evolution unfolds in three phases:

• AI-assisted. Operators interact with Dynatrace directly from ServiceNow using natural language, accessing observability insights in context. This capability is available today through integrated workflows.
• AI-led. Agents begin coordinating workflows across platforms autonomously, while maintaining human oversight.
• AI-driven. Agents validate hypotheses, assess business impact, and execute full remediation workflows automatically — realizing the vision of autonomous IT.

This approach builds trust gradually, delivering immediate value today while preparing organizations for the agentic future of operations.

The convergence that makes autonomous operations possible

Dynatrace provides in-bound intelligence, combining deterministic and agentic AI for precise root-cause analysis, predictive detection, and proactive remediation across modern, multicloud environments.

“By bringing together real-time, AI-powered observability from Dynatrace with ServiceNow’s AI-powered IT Service & Operations Management, we’re empowering IT teams to move beyond traditional operations into a new era of proactive systems that continuously learn, adapt, and self-heal at scale.”
— Rahul Tripathi, group vice president and general manager, ITSM and ITOM at ServiceNow

ServiceNow provides out-bound intelligence, transforming observability insights into immediate, reliable action. Incidents are automatically created, enriched, routed, and remediated with full business context, reducing MTTR and operational overhead.

Here’s how the combined solution enables closed-loop operations:

1. Proactive detection and response. Dynatrace Davis® AI continuously detects performance, availability, and resource anomalies before they impact users. When a problem arises, ServiceNow automatically generates a context-rich ticket, complete with root cause, dependency mapping, and business impact, eliminating manual triage and ensuring the right teams act fast.
2. Continuous environment synchronization. Dynatrace Smartscape® automatically maps every service and dependency across your environment. Through the Service Graph Connector for Observability – Dynatrace, that real-time topology feeds directly into the ServiceNow CMDB, keeping it accurate and actionable without manual updates.
3. Closed-loop automation. When Dynatrace detects specific problems, such as memory saturation, capacity constraints, deployment anomalies, ServiceNow workflows automatically initiate remediation actions. Each workflow verifies the outcome via Dynatrace APIs, confirming the root cause is resolved, not just masked.

This is closed-loop remediation in practice: detect, diagnose, act, validate, and learn – continuously.

When self-healing IT meets real-world scale

Organizations worldwide are already realizing measurable gains from this unified approach:

• CareSource reduced MTTR by >98% and cut downtime from 12 hours to 2 through automated self-healing workflows powered by Dynatrace and ServiceNow, while increasing observability adoption by 450%.
• BT Digital achieved a 93% reduction in mean time to detection and resolution. When a critical Apache process failed, Dynatrace detected it in 2 minutes, and ServiceNow remediated it automatically in under 6.
• Commerzbank realized a 70% reduction in major incidents and 96% faster MTTR – from 30 hours to 1 – as part of its journey toward ticket-free IT operations. (October 2024)

These are strong indicators of a deeper transformation, from reactive operations to proactive, self-healing systems that elevate the role of IT from maintenance to innovation.

A trusted foundation for Zero Outage outcomes

The Dynatrace and ServiceNow partnership creates a reliable, AI-powered foundation for Zero Outage outcomes. It combines:

• Davis® AI, the Dynatrace hypermodal AI engine for deterministic root-cause analysis and predictive insights.
• Smartscape®, dynamic topology mapping that continuously updates ServiceNow’s CMDB.
• OneAgent®, unified instrumentation for complete, code-level observability across any environment.

Together, these capabilities give ServiceNow workflows the trustworthy, real-time context needed to act autonomously. As organizations mature, they evolve through stages of operational intelligence, from visibility to automation to prediction, ultimately achieving self-healing IT ecosystems.

For executives, this means lower operational costs and higher reliability. For ITOps, SRE, and platform teams, it means faster resolution, fewer alerts, and more time for strategic innovation.

Getting started: from insight to autonomy

The path to autonomous operations begins with a solid observability foundation:

1. Deploy Dynatrace OneAgent® for full-stack visibility.
2. Integrate ServiceNow using certified apps available in the ServiceNow Store:
   • Service Graph Connector for Observability – Dynatrace
   • Dynatrace Incident Integration
   • Dynatrace Workflows for ServiceNow
3. Automate remediation for common, high-frequency scenarios.
4. Expand continuously toward predictive and self-healing automation.

Put autonomous operations into practice

Experience how Dynatrace and ServiceNow combine intelligent observability and automation to turn reactive operations into proactive innovation.

The post Intelligence in, intelligence out: How Dynatrace and ServiceNow are powering autonomous IT appeared first on Dynatrace news.

Over one million organizations use Microsoft Teams as their default communication platform, resulting in a significant surge in user numbers to over 320 million worldwide in 2024. As such, MS Teams has its own set of challenges in terms of triaging, troubleshooting, and diagnosing disruptions. Phenisys has answered that challenging call and built an intricate Microsoft Teams Observability app that can help organizations answer questions like “Are you getting the best Microsoft Teams experience?”, or “Would you even know if you weren’t getting the best experience?”

Various challenges with effective Microsoft Teams observability include:

• The wide variety of potentially impacted components, for example, a network outage, an issue with a local device, or a larger outage.
• The spread of teams and employees that would potentially be included in the diagnosing and troubleshooting process, for example, SREs, IT Ops, helpdesk, or network teams.
• Native tools and data can be limited, making the correlation of user experience with potential backend issues complex.

Phenisys addresses these challenges with an app that combines AI-powered analysis, enriched metrics, and contextual data—giving teams fast and actionable insights.

In this blog post, you’ll learn how to utilize the deeply integrated features to diagnose any Microsoft Teams call issue in only three clicks:

1. Identify the troublesome call (by user name or call ID)
2. Review the call’s waterfall timeline for abnormalities
3. Diagnose the root cause of the issue(s)

Streamlined insights into Microsoft Teams performance

For the ingenuity and potential impact of their app, Phenisys was awarded first place in the 2024 Dynatrace® Partner App Competition, where partners were challenged to utilize Dynatrace AppEngine to solve real-world customer use cases. With recent statistics showing around 2.7 billion virtual meeting minutes every day (equivalent to over 5,000 years), this is certainly a high-demand use case.

Key features include:

• Home page
  Surface call-quality trends across your organization to proactively address issues before they escalate to ensure low downtime and improved user satisfaction.
• Site overview page
  Pinpoint performance hotspots and recurring issues globally across your locations to streamline troubleshooting and prioritize your investments in infrastructure.
• Call diagnostics page
  Isolate problematic calls and empower support teams to resolve issues without escalation to cut MTTR and support costs.
• Call record overview page
  Gain full visibility into user impact and root cause in minutes, allowing faster MTTR and better communication with affected teams.
• Microsoft issues page
  Stay ahead of service disruptions with real-time visibility into Microsoft-reported issues, tailored to your tenant, allowing you to inform users and adjust operations proactively.
• Configuration page
  Adjust app settings to your organization’s compliance needs and operational workflows to ensure relevance and maximum value.

Use cases

Suppose you’re on an important Teams call when you’re suddenly dropped from the call. While trying to get back in, you check with a coworker who was also on the call; they report audio issues, and someone else spams the call chat with a message stating that the screen share is frozen. As a result of this unexpectedly heavily bugged Teams call, the meeting had to be postponed, and tickets are piling up with the helpdesk.

With standard Microsoft Admin tools, the helpdesk agent can review aggregated metrics, which don’t report anything abnormal. According to the agent’s metrics, the call should have been fine, so they escalated it to the network team. Without any qualified leads, the network team has to go blind and look for possible causes. It takes two hours for someone to discover that the local WiFi router was overloaded and caused these issues. The fix is simple, but users are frustrated, and time was wasted.

With the Microsoft Teams Observability app, the same ticket can be resolved in three simple steps:

1. The helpdesk agent brings up the call either by username or call ID to check available metadata and quality indicators, such as “health.”

   

2. The agent then opens the details page to get the call timeline, including information about the performance of audio, video, and screen-sharing streams. We see a clear video degradation within the first ten minutes of the call. Thanks to the waterfall view, the agent already knows how and when the issue occurred for the user.

   

3. The agent then drills into the details. All impacted users are in the same office, so network metrics are the first point of investigation. The app highlights an abnormally high latency for that office, which is not aligned with best practices recommended by Microsoft. Within a few minutes, the agent can identify a faulty WiFi access point as a root cause, providing a clear resolution path without the need for escalation.

   

In the end, finding the root cause of the call degradation took only three clicks and mere minutes instead of hours on a wild goose chase, and instead of escalating the issue to the network team, the helpdesk agent was able to solve this on their own.

With the Microsoft Teams Observability app, workflows for operations and helpdesk teams are transformed: no more blind troubleshooting with direct and actionable insights into metrics and root cause. Tickets are no longer a black box.

Best practices

On top of all the features available, you can get even more value out of the app by utilizing a few key features and tweaking the setup to your needs.

• Utilize pre-built dashboards! To ease users into the app experience, Phenisys provides two dashboards, tracking MS Teams versions used across devices, as well as locations and users utilizing TCP streams (since Microsoft recommends using UDP ports instead of TCP). This helps users track performance over time.
• Correlate data from different call center components! With the app’s capabilities, you can view audio attendance, call queues, PSTN, and even direct routing directly within the app.
• Adjust retention periods to your needs! The app allows you certain flexibility in data retention, which can optimize data storage and support you in your efforts to align with data privacy and compliance requirements.
• Enrich data with context! Utilize lookup files to enrich your existing data with client context, ensuring more accurate and actionable insights.

Further reading

Get the app from the Hub:

• Microsoft Teams Observability

Get in touch with Phenisys:

• Phenisys partner page

Other apps built by Phenisys:

• Alicloud Extension

Other partner blog posts:

• Omnilogy Pipeline Observability
• Dynatrace® Apps showcase: Akamas Kubernetes optimization

Public videos:

• Promotional video Phenisys: Microsoft Teams Observability by Phenisys
• Dynatrace Observability Lab: Optimize Your Microsoft Teams Experience with Real-Time Insights

The post Dynatrace® Apps showcase: Phenisys MS Teams Observability appeared first on Dynatrace news.

Enterprise adoption of generative AI is showing no signs of slowing down, and it’s easy to understand why; organizations in every vertical aim to reap its benefits, including increased efficiency, routine task automation, and content generation, ultimately creating a competitive advantage. To better help organizations maximize the benefit and full potential of generative AI, Dynatrace has achieved the Amazon Web Services Generative AI (GenAI) Competency.

With this milestone, Dynatrace reinforces its position as a leading observability partner, backed by a proven track record of innovation and customer success on AWS. Building on its achievement of earning the AWS Machine Learning Competency, Dynatrace continues to drive advancements in generative AI.

Weighing the importance of this milestone for Dynatrace customers

This competency is more than just a badge; it’s a validation of how Dynatrace can help organizations safely, efficiently, and cost-effectively adopt generative AI in their business. AWS awards these competencies after rigorous technical validation and proven customer success. This means organizations can trust that Dynatrace solutions are designed to deliver measurable outcomes on AWS.

For existing customers, this competency reaffirms the Dynatrace commitment to continued innovation alongside AWS. This ensures the Dynatrace AI-powered observability platform evolves with the latest advancements in AI, future-proofing organizations’ existing investments as generative AI capabilities become core to modern cloud workloads.

For new customers, Dynatrace provides a trusted, proven foundation for observability and AI adoption on AWS. Whether an organization is exploring GenAI for customer engagement, automation, or new digital experiences, Dynatrace ensures these systems are reliable, secure, and optimized at every step.

Looking ahead with AI-powered observability on AWS

As organizations increasingly adopt generative AI, observability becomes a critical enabler. By leveraging Dynatrace causal AI, predictive insights, and seamless AWS integrations, organizations can maintain control over costs, risks, and performance while driving innovation, enhancing competitive advantage, and delivering exceptional customer experiences.

Whether you’re building, scaling, or fine-tuning GenAI application, Dynatrace and AWS Bedrock empower you to transform your observability. With end-to-end visibility into AI workloads, their interactions in full context of your business, and cloud-native applications, you can optimize performance, troubleshoot effectively, and maximize the value of your GenAI investments with greater confidence and precision.

Learn more

• Davis AI
• Dynatrace for Amazon Bedrock
• Explore the Bedrock Monitoring Playground to get hands-on with real-time dashboards that reveal how your Amazon Bedrock models behave in production.
• Install the AI Observability app to integrate Dynatrace’s AI Observability app into your environment to monitor GenAI workloads.
• Stop by booth #575 at AWS re:Invent 2025 to see Dynatrace in action.

The post Dynatrace achieves AWS Generative AI Competency: A new milestone in observability and AI appeared first on Dynatrace news.

A generational technology shift is afoot—one where AI-powered workloads are the new currency of innovation. Agentic applications—built on foundation models, APIs, and autonomous workflows—are transforming how businesses create, deliver, and scale value.

Enterprise leaders are no longer asking if AI will play a central role in their strategy. Rather, they’re asking how fast they can operationalize AI to drive personalization, automation, and competitive advantage.

From observability to actionable intelligence

The key to unlocking AI’s full potential lies in understanding what your data is telling you. AI-native organizations don’t just need visibility; they need context, causality, and automation across their digital systems.

This is where Dynatrace excels. By combining full-stack observability with causal AI and advanced analytics, Dynatrace can help enterprises move beyond dashboards toward self-healing systems, intelligent automation, and AI-powered experiences that continuously improve.

Enhance agentic solutions with Dynatrace through the AWS Agentic AI Marketplace

To accelerate your journey in this AI-driven era, Dynatrace is proud to be part of the Amazon Web Services (AWS) Agentic AI Marketplace—Amazon’s emerging ecosystem designed to bring together agentic applications, reusable APIs, data sets, and generative AI solutions.

This integration delivers significant value, including the following:

• Seamless integration for AI workflows. Autonomous agents and builders can now readily discover and connect with Dynatrace, making it easier to compose sophisticated, intelligent workflows.
• Empower AI agents. AI agents building real-time, adaptive solutions can directly invoke Dynatrace observability, security, and automation capabilities, enabling them to make more informed decisions and take effective actions.
• Faster innovation. Enterprises benefit from faster time to value, as Dynatrace solutions are now natively composable within AWS AI services, such as Amazon Q, Amazon Bedrock, and Amazon SageMaker.

Ultimately, the AWS Agentic AI Marketplace empowers AI agents to dynamically discover, invoke, and orchestrate trusted services like Dynatrace, providing the foundation for an  autonomous and intelligent enterprise.

How Dynatrace enables business value in the agentic era

With the Dynatrace® AI-powered observability platform, organizations can benefit from the following capabilities:

• Smarter, safer automation. Dynatrace provides real-time insights into system health, anomalies, and dependencies—empowering agents to take autonomous action without compromising reliability or security.
• Accelerated AI decision-making. Dynatrace data streams feed foundational models and agentic workflows with high-fidelity, context-rich information—leading to faster, more accurate decision-making at scale.
• Personalized digital experiences. By understanding user behavior, performance trends, and business context, Dynatrace enables agents to dynamically personalize digital experiences in real time.
• Continuous optimization. From cost-aware workload placement to real-time cloud performance tuning, Dynatrace allows enterprises to scale AI workloads with confidence and efficiency.

Answering the call to innovation

The future of enterprise software is here: agentic, composable, and autonomous. For leaders navigating this massive shift, the question isn’t whether AI will play a central role, but how quickly teams can operationalize it to drive competitive advantage.

Now is the time to evaluate if your systems are truly ready—not just for AI adoption, but for AI collaboration. In this new ecosystem, it’s not simply about building AI; it’s about building with AI. This demands partners who can unlock the full potential of your data, enable your systems to adapt seamlessly, and accelerate your business outcomes. With Dynatrace, you gain a partner actively helping businesses operationalize trust, observability, and AI at scale.

The post How Dynatrace drives value in the age of AI in the AWS® Agentic AI Marketplace appeared first on Dynatrace news.

The need for application and DevOps modernization to deliver on business outcomes has never been greater. Organizations are increasingly embracing cloud- and AI-native strategies, requiring a more automated and intelligent approach to their observability and development practices. That’s why Dynatrace has made its AI-powered observability platform generally available on Google Cloud for all customers.

Customers can now experience all the latest Dynatrace platform features, including the Grail data lakehouse, Davis AI, and unrivaled log analytics, on Google Cloud. Organizations get deep insights into any workload — whether in the cloud or on premises. That means customers can operate Dynatrace directly on Google Cloud, which might often be the hyperscaler where most of their workload stays.

The power of Dynatrace AI-powered observability on Google Cloud

The latest Dynatrace core innovations available on Google Cloud include the following:

• Dynatrace Grail. The data lakehouse unifies the massive volume and variety of observability, security, and business data from cloud-native, hybrid, and multicloud environments while retaining data context to deliver instant, cost-efficient, and precise analytics.
• Dynatrace AutomationEngine. Featuring a no- and low-code toolset, AutomationEngine leverages Davis AI to empower teams to create and extend customized, intelligent, and secure workflow automation across cloud ecosystems.
• Dynatrace AppEngine. This features a no- and low-code toolset and leverages Davis AI to empower teams to easily create and share custom, intelligent, and secure apps that leverage insights from data generated by their clouds.
• The new Dynatrace user experience. Including powerful dashboarding capabilities, interactive Dynatrace Notebooks, and tailor-made apps that provide an actionable and easy-to-digest view, the Dynatrace user experience drives tighter cross-team collaboration and allows more people within the organization to make data-backed decisions.

The Dynatrace platform on Google Cloud is available for all Dynatrace customers from the two already-supported regions:

• us-east4 (N. Virginia)
• Europe-west3 (Frankfurt)

Existing Dynatrace customers on Google Cloud can activate the new features with a single click, and new customers will get the latest experience by default.

Go deeper into distributed and Google Cloud workloads

Customers can now access the latest version of Dynatrace SaaS, which is also available on AWS and Microsoft Azure. Now natively operated on Google Cloud, customers can leverage all the unique features of the AI-powered observability platform to get deep insights into cross-cloud and on-premises workloads, with additional benefits out of the box for customers having workloads on Google Cloud:

• Dynatrace Grail offers a single store for logs, events, metrics, spans, traces, user actions, and sessions as the only data lakehouse combining observability, security, and business data.
• The Dynatrace Clouds app provides a complete inventory of cross-cloud and cross-account resources.
• Dynatrace OneAgent allows teams to observe Google Kubernetes Engine pods, nodes, clusters and workload metrics, events, and logs, in addition to automated distributed tracing for applications and microservices.
• Cloud Security Posture Management boosts security, compliance, and resource efficiency with continuous monitoring, automated remediation, and centralized visibility for organizations managing complex hybrid and multicloud environments.
• The new Dashboards allow teams to explore real-time, AI-powered views of a Google Cloud workload.
• DQL is a powerful tool to explore data across multicloud environments and Google Cloud workloads in particular.
• The Infrastructure & Operations app provides an up-to-date and comprehensive view of monitored environments on Google Cloud.
• The Discovery & Coverage app allows users to detect blind spots and implement the appropriate level of observability for workloads.

Purchasing Dynatrace via the Google Cloud Marketplace allows teams to utilize their Google Committed Use Discounts.

Visualize the state of Google Cloud using Dashboards

Dynatrace Dashboards, the new real-time and AI-powered data exploration experience, allow users to visualize and explore the state of their Google Cloud infrastructure easily. To get started, use a ready-made dashboard and adapt it as needed. The following screenshots show the dashboard in action, offering detailed insights into a Google Cloud workload, either compute or non-compute resources, and an overview of the problems identified by Dynatrace Davis AI.

The Dynatrace Clouds app

The Dynatrace Clouds app offers an intuitive and actionable cloud resource inventory view of Google Cloud workloads and all workloads deployed cross-cloud and cross-account. The following screenshot shows the Clouds app providing a comprehensive means to filter and search a cloud inventory.

The Infrastructure & Operations app

The Infrastructure & Operations app offers the most current view of all monitored workloads. It allows users to spot ongoing problems and dive into their details, as shown in the following screenshot.

Spend Google Committed Use Discounts with Dynatrace

Users with a Google Committed Use Discount can purchase Dynatrace directly via the Google Cloud Marketplace, spending your pre-commit. For further information, check out Dynatrace on the Google Cloud Marketplace.

Learn how to observe Google Cloud workloads with the Dynatrace platform

To learn how to monitor your Google Cloud workload with Dynatrace, please read our guide, “Set up Dynatrace on Google Cloud.”

If you’re an existing Dynatrace customer, please contact us to learn how to transition to the latest version of Dynatrace on Google Cloud.

© 2025 Dynatrace LLC.

Dynatrace, Grail, Davis, and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies.  All other trademarks are the property of their respective owners.

The post The latest Dynatrace AI-powered observability features are now on Google Cloud appeared first on Dynatrace news.