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Dynatrace and Microsoft Partner to Scale Enterprise Customer AI Initiatives

New integration enables customers to automate majority of cloud operations tasks

Dynatrace announced a new integration between the Dynatrace platform and Microsoft Azure SRE Agent, Microsoft’s AI-powered reliability assistant for Azure that continuously monitors resources. 

Dynatrace is the first observability platform to integrate with Azure SRE Agent, setting a new standard for cloud operations.

According to Gartner®, “worldwide spending on AI is forecast to total nearly $1.5 trillion in 2025.” As organizations continue to accelerate their AI investments, gaining end-to-end visibility into the entire digital ecosystem and automating operations is increasingly critical. With the agentic integration of Dynatrace in the Azure portal, customers are empowered to achieve these goals with greater confidence.

The integration combines AI-driven root cause analysis from Dynatrace with deeper insights from Azure SRE, allowing teams to fix complex problems in large-scale IT environments faster and more efficiently. The integration also unlocks streamlined remediation processes, including remediation hints, to support accelerated incident resolution and reduced outages. This allows teams to focus on innovation and driving the business forward.

Key benefits of the integration include:

  • Smarter detection and remediation:  Deep contextual observability from Dynatrace correlates with Azure telemetry to enhance issue identification and resolution across complex environments.
  • Automated operations: Routine runbook actions and diagnostic workflows are automated, reducing mean time to repair and freeing teams to focus on innovation.
  • Proactive reliability: Continuous analysis of real-time and historical data reveals leading indicators of failure, helping teams prevent incidents before they impact customers.

“The AI capabilities jointly delivered by Dynatrace and Microsoft take our customers one step closer to driving autonomous operations across their complex environments,” said Scott Hunter, VP, Product Management of Core AI & Engineering at Microsoft. “With continuous, automatic real-time insights and analysis, teams have more time to focus on driving innovation.”  

“Enterprises today are operating in increasingly complex cloud environments, where disparate systems and lack of visibility can hinder innovation,” said Steve Tack, Chief Product Officer at Dynatrace. “Customers need more than alerts – they need AI that acts. This integration strengthens Dynatrace’s vision for agentic AI, delivering intelligent, automated observability across the Microsoft ecosystem, and helps enterprises not only identify issues, but automate remediation at scale.”

Dynatrace customer FreedomPay will co-present on its generative and agentic AI vision during Microsoft Ignite, November 18–21, 2025.  Visit Dynatrace at booth 5438 to see a demo, join Dynatrace Quest, meet with an expert, or learn how Dynatrace can help you achieve seamless observability. 

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Dynatrace and Microsoft Partner to Scale Enterprise Customer AI Initiatives

New integration enables customers to automate majority of cloud operations tasks

Dynatrace announced a new integration between the Dynatrace platform and Microsoft Azure SRE Agent, Microsoft’s AI-powered reliability assistant for Azure that continuously monitors resources. 

Dynatrace is the first observability platform to integrate with Azure SRE Agent, setting a new standard for cloud operations.

According to Gartner®, “worldwide spending on AI is forecast to total nearly $1.5 trillion in 2025.” As organizations continue to accelerate their AI investments, gaining end-to-end visibility into the entire digital ecosystem and automating operations is increasingly critical. With the agentic integration of Dynatrace in the Azure portal, customers are empowered to achieve these goals with greater confidence.

The integration combines AI-driven root cause analysis from Dynatrace with deeper insights from Azure SRE, allowing teams to fix complex problems in large-scale IT environments faster and more efficiently. The integration also unlocks streamlined remediation processes, including remediation hints, to support accelerated incident resolution and reduced outages. This allows teams to focus on innovation and driving the business forward.

Key benefits of the integration include:

  • Smarter detection and remediation:  Deep contextual observability from Dynatrace correlates with Azure telemetry to enhance issue identification and resolution across complex environments.
  • Automated operations: Routine runbook actions and diagnostic workflows are automated, reducing mean time to repair and freeing teams to focus on innovation.
  • Proactive reliability: Continuous analysis of real-time and historical data reveals leading indicators of failure, helping teams prevent incidents before they impact customers.

“The AI capabilities jointly delivered by Dynatrace and Microsoft take our customers one step closer to driving autonomous operations across their complex environments,” said Scott Hunter, VP, Product Management of Core AI & Engineering at Microsoft. “With continuous, automatic real-time insights and analysis, teams have more time to focus on driving innovation.”  

“Enterprises today are operating in increasingly complex cloud environments, where disparate systems and lack of visibility can hinder innovation,” said Steve Tack, Chief Product Officer at Dynatrace. “Customers need more than alerts – they need AI that acts. This integration strengthens Dynatrace’s vision for agentic AI, delivering intelligent, automated observability across the Microsoft ecosystem, and helps enterprises not only identify issues, but automate remediation at scale.”

Dynatrace customer FreedomPay will co-present on its generative and agentic AI vision during Microsoft Ignite, November 18–21, 2025.  Visit Dynatrace at booth 5438 to see a demo, join Dynatrace Quest, meet with an expert, or learn how Dynatrace can help you achieve seamless observability. 

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...