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Dynatrace and Postman Empower Developers with AI-Powered Observability in Agent Mode

Dynatrace announced that developers using Postman Agent Mode can now access real‑time observability and production context directly within their AI‑assisted API workflows, helping teams improve API quality, reliability, and speed of delivery.

This capability is enabled through an expansion of Dynatrace's technology alliance with Postman.

As part of this expanded alliance, the Dynatrace Model Context Protocol (MCP) Server will be available in the Postman API Network, enabling developers to securely connect Agent Mode with Dynatrace observability data. This integration allows Postman to surface trusted telemetry, correlate API behavior with live production data, and automate troubleshooting without developers needing to leave the Postman environment.

Postman Agent Mode is a native AI agent grounded in context from existing collections, code, and governance standards to help teams build, test, and manage APIs. By connecting Agent Mode to the Dynatrace MCP Server, teams can surface real‑time observability data and insights across the API lifecycle. With Dynatrace as the observability backend, developers gain live visibility into API behavior and performance directly within their existing workflows.

“Connecting Dynatrace to Agent Mode brings AI‑powered observability directly into a tool developers already trust and use every day,” said Bonifaz Kaufmann, Vice President of Product at Dynatrace. “Developers can now link API behavior to real production telemetry, understand issues in context, and resolve problems faster so they can focus on building and innovating.”

Using Postman Agent Mode and the Dynatrace MCP Server, teams can enable an AI agent to test APIs, correlate failures with live telemetry data, explain root cause, and resolve issues in natural language, all within a single workflow. This streamlined approach helps teams reduce friction between development and operations while improving overall API quality.

“Agent Mode represents an entirely new way of using Postman with full context across the API lifecycle,” said Balaji Raghavan, Head of Engineering at Postman. “Our valued partners play a critical role in extending that context even further to support developers' API innovation. Dynatrace's AI-powered observability platform provides deep insights and automation into API development, helping developers design, build and optimize higher quality APIs.”

Dynatrace supports many of the world's largest enterprises, including a significant portion of the Fortune 500, and serves as a broad global developer community. With strong overlap among organizations already using Postman, the availability of the Dynatrace MCP Server expands Dynatrace's reach within a highly engaged API ecosystem.

The Dynatrace MCP Server is now available via the Postman API Network. 

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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Dynatrace and Postman Empower Developers with AI-Powered Observability in Agent Mode

Dynatrace announced that developers using Postman Agent Mode can now access real‑time observability and production context directly within their AI‑assisted API workflows, helping teams improve API quality, reliability, and speed of delivery.

This capability is enabled through an expansion of Dynatrace's technology alliance with Postman.

As part of this expanded alliance, the Dynatrace Model Context Protocol (MCP) Server will be available in the Postman API Network, enabling developers to securely connect Agent Mode with Dynatrace observability data. This integration allows Postman to surface trusted telemetry, correlate API behavior with live production data, and automate troubleshooting without developers needing to leave the Postman environment.

Postman Agent Mode is a native AI agent grounded in context from existing collections, code, and governance standards to help teams build, test, and manage APIs. By connecting Agent Mode to the Dynatrace MCP Server, teams can surface real‑time observability data and insights across the API lifecycle. With Dynatrace as the observability backend, developers gain live visibility into API behavior and performance directly within their existing workflows.

“Connecting Dynatrace to Agent Mode brings AI‑powered observability directly into a tool developers already trust and use every day,” said Bonifaz Kaufmann, Vice President of Product at Dynatrace. “Developers can now link API behavior to real production telemetry, understand issues in context, and resolve problems faster so they can focus on building and innovating.”

Using Postman Agent Mode and the Dynatrace MCP Server, teams can enable an AI agent to test APIs, correlate failures with live telemetry data, explain root cause, and resolve issues in natural language, all within a single workflow. This streamlined approach helps teams reduce friction between development and operations while improving overall API quality.

“Agent Mode represents an entirely new way of using Postman with full context across the API lifecycle,” said Balaji Raghavan, Head of Engineering at Postman. “Our valued partners play a critical role in extending that context even further to support developers' API innovation. Dynatrace's AI-powered observability platform provides deep insights and automation into API development, helping developers design, build and optimize higher quality APIs.”

Dynatrace supports many of the world's largest enterprises, including a significant portion of the Fortune 500, and serves as a broad global developer community. With strong overlap among organizations already using Postman, the availability of the Dynatrace MCP Server expands Dynatrace's reach within a highly engaged API ecosystem.

The Dynatrace MCP Server is now available via the Postman API Network. 

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.