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Dynatrace Enhances Developer Experience to Power Smarter Agentic Workflows and Frontend Innovation

New capabilities transform observability into an active system of control for cloud and AI-native software delivery

At Perform, its flagship annual user conference, Dynatrace announced a deeply integrated set of developer experience and agentic AI capabilities. 

These updates evolve observability from passive insight into an intelligent control layer that actively guides, optimizes, and protects software delivery in real time.

Software delivery is undergoing a major shift as cloud-native architectures, AI-assisted development, and agentic systems accelerate release of velocity. According to recent Dynatrace research, product development is the fastest-growing use case for agentic AI, with 42% of organizations already using it in customer-facing digital products and another 31% planning to expand adoption over the next five years. As code, models, prompts, and configurations change faster and behave more dynamically, teams need not just visibility, but real-time, production-grade control to act confidently, manage risk, and keep applications stable at enterprise scale.

The capabilities announced today unify frontend, backend, AI telemetry, database, cloud, and mobile into a single developer-facing experience built on Dynatrace Grail™, Smartscape, and Dynatrace Intelligence. Dynatrace’s AI-native foundation – built for agentic and LLM-driven applications – enables humans and AI agents to reason about live system behavior, experiment safely in production, and take immediate, targeted action without redeployments or manual intervention.

Key enhancements include:

  • Frontend observability with RUM: A modernized frontend experience unifies RUM data in Grail™ and introduces three new apps, including Error Inspector, to give developers deeper visibility and faster troubleshooting of real user behavior and frontend issues.
  • Expanded mobile developer capabilities: New mobile diagnostics provides the context developers need to pinpoint Application Not Responding (ANRs) events and crashes quickly, reducing debugging time and improving app stability.
  • Software Delivery and Observability in the Age of AI: With the acquisition of DevCycle, Dynatrace advances observability into an active system of control. DevCycle’s precise, feature-level runtime controls combine with Dynatrace Intelligence to continuously validate behavior, reduce risk, and automatically respond to issues from development through production. Integration work for DevCycle is already underway with further updates in the future as it relates to release.
  • End-to-end traces across AI, databases, and cloud platforms: Unified tracing links AI calls, application services, databases, and cloud infrastructure, providing clarity as AI-driven workloads increase trace volume and complexity across distributed systems.
  • Agentic workflows and MCP integrations: Agentic workflows and the Dynatrace MCP Server enable developers and AI agents to act safely on real-time observability data. MCP support for Claude, AWS Bedrock AgentCore, and Azure AI Foundry, delivering safe automation across multi-cloud and multi-AI environments.
  • Live Debugger updates: Expanded support for leading IDEs, including Windsurf and Cursor, bring instant access to live debugging within the programming environment, streamlining development and troubleshooting

“Developers within our organization are spending significantly less time now debugging, coordinating, and troubleshooting thanks to Dynatrace’s developer experience capabilities, including Live Debugger,” explains Dana Harrison at TELUS. “By reducing time and focus in these areas, we’ve been able to pivot efforts to build high-quality software with greater confidence than ever, delivering innovations faster for our business. This has all been done with no increase in risk. As we look to the future, we know we can increase developer productivity within our organization thanks to Dynatrace, confidently supporting us on our journey to improve social outcomes through technology.”

“In the era of vibe coding and AI-assisted engineering, developer experience is a critical business driver, not a mere operational concern,” said Kate Holterhoff, senior industry analyst at RedMonk. “With developers increasingly relying on AI for code generation, the importance of ensuring the quality and performance of that output—especially on the frontend and mobile interfaces—is amplified. Dynatrace’s observability enhancements in this area including Real User Monitoring (RUM), Error Inspector, and ANR/crash symbolization supports DevX by providing practitioners with real-time context into user behavior and production issues.”

“Developers play a defining role in how organizations innovate and shape the technology investments that drive long-term value,” said Steve Tack, Chief Product Officer at Dynatrace. “The latest enhancements from Dynatrace enhance developer impact, improve release confidence, and operationalize software delivery across cloud native, AI native, and multicloud environments. By unifying delivery, runtime control, and insight on a single platform, we give developers direct control over how software behaves in production—so they can experiment safely, respond quickly, and turn real-world signals into real business impact.”

Several enhancements are available immediately, while others will be rolled out over time.

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Dynatrace Enhances Developer Experience to Power Smarter Agentic Workflows and Frontend Innovation

New capabilities transform observability into an active system of control for cloud and AI-native software delivery

At Perform, its flagship annual user conference, Dynatrace announced a deeply integrated set of developer experience and agentic AI capabilities. 

These updates evolve observability from passive insight into an intelligent control layer that actively guides, optimizes, and protects software delivery in real time.

Software delivery is undergoing a major shift as cloud-native architectures, AI-assisted development, and agentic systems accelerate release of velocity. According to recent Dynatrace research, product development is the fastest-growing use case for agentic AI, with 42% of organizations already using it in customer-facing digital products and another 31% planning to expand adoption over the next five years. As code, models, prompts, and configurations change faster and behave more dynamically, teams need not just visibility, but real-time, production-grade control to act confidently, manage risk, and keep applications stable at enterprise scale.

The capabilities announced today unify frontend, backend, AI telemetry, database, cloud, and mobile into a single developer-facing experience built on Dynatrace Grail™, Smartscape, and Dynatrace Intelligence. Dynatrace’s AI-native foundation – built for agentic and LLM-driven applications – enables humans and AI agents to reason about live system behavior, experiment safely in production, and take immediate, targeted action without redeployments or manual intervention.

Key enhancements include:

  • Frontend observability with RUM: A modernized frontend experience unifies RUM data in Grail™ and introduces three new apps, including Error Inspector, to give developers deeper visibility and faster troubleshooting of real user behavior and frontend issues.
  • Expanded mobile developer capabilities: New mobile diagnostics provides the context developers need to pinpoint Application Not Responding (ANRs) events and crashes quickly, reducing debugging time and improving app stability.
  • Software Delivery and Observability in the Age of AI: With the acquisition of DevCycle, Dynatrace advances observability into an active system of control. DevCycle’s precise, feature-level runtime controls combine with Dynatrace Intelligence to continuously validate behavior, reduce risk, and automatically respond to issues from development through production. Integration work for DevCycle is already underway with further updates in the future as it relates to release.
  • End-to-end traces across AI, databases, and cloud platforms: Unified tracing links AI calls, application services, databases, and cloud infrastructure, providing clarity as AI-driven workloads increase trace volume and complexity across distributed systems.
  • Agentic workflows and MCP integrations: Agentic workflows and the Dynatrace MCP Server enable developers and AI agents to act safely on real-time observability data. MCP support for Claude, AWS Bedrock AgentCore, and Azure AI Foundry, delivering safe automation across multi-cloud and multi-AI environments.
  • Live Debugger updates: Expanded support for leading IDEs, including Windsurf and Cursor, bring instant access to live debugging within the programming environment, streamlining development and troubleshooting

“Developers within our organization are spending significantly less time now debugging, coordinating, and troubleshooting thanks to Dynatrace’s developer experience capabilities, including Live Debugger,” explains Dana Harrison at TELUS. “By reducing time and focus in these areas, we’ve been able to pivot efforts to build high-quality software with greater confidence than ever, delivering innovations faster for our business. This has all been done with no increase in risk. As we look to the future, we know we can increase developer productivity within our organization thanks to Dynatrace, confidently supporting us on our journey to improve social outcomes through technology.”

“In the era of vibe coding and AI-assisted engineering, developer experience is a critical business driver, not a mere operational concern,” said Kate Holterhoff, senior industry analyst at RedMonk. “With developers increasingly relying on AI for code generation, the importance of ensuring the quality and performance of that output—especially on the frontend and mobile interfaces—is amplified. Dynatrace’s observability enhancements in this area including Real User Monitoring (RUM), Error Inspector, and ANR/crash symbolization supports DevX by providing practitioners with real-time context into user behavior and production issues.”

“Developers play a defining role in how organizations innovate and shape the technology investments that drive long-term value,” said Steve Tack, Chief Product Officer at Dynatrace. “The latest enhancements from Dynatrace enhance developer impact, improve release confidence, and operationalize software delivery across cloud native, AI native, and multicloud environments. By unifying delivery, runtime control, and insight on a single platform, we give developers direct control over how software behaves in production—so they can experiment safely, respond quickly, and turn real-world signals into real business impact.”

Several enhancements are available immediately, while others will be rolled out over time.

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...