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Dynatrace Adds Agentic AI Capabilities

Dynatrace is extending the Dynatrace platform with agentic AI capabilities. 

Designed to predict and prevent disruptions, protect systems and data, and optimize operations autonomously, these advancements mark a new era of productivity and agility, fundamentally redefining how businesses manage digital transformation.

Dynatrace leverages agentic AI to enabling enterprises to transition from manual oversight to autonomous, AI-driven workflows that streamline operations and foster innovation.

“We anticipated the growing complexity of digital systems outpacing the capabilities of traditional observability solutions reliant on human intervention,” said Bernd Greifeneder, Founder and CTO, Dynatrace. “This is why we built the next generation of our platform to help customers leverage advanced AI to offload work and unlock entirely new possibilities. By unifying observability, security, and business data in a revolutionary data lakehouse architecture, we’ve created the foundation for AI to deliver real-time insights and act autonomously in ways that were unimaginable a few years ago.”

The core of Dynatrace is a purpose-built foundation for agentic AI, seamlessly unifying observability, security, and business data in a schema-free, indexless data lakehouse. This foundation empowers intelligent decision-making and autonomous action, enabling enterprises to transition from human oversight to intelligent, self-operating systems. Together, the platform serves as the knowledge, reasoning, planning, and actioning framework of agentic AI, delivering trustworthy precision and adaptability.

  • Grail, an indexless, schema-free data lakehouse, hot/hot by design, provides real-time access to petabytes of data in context, eliminating the inefficiencies of re-indexing and rehydration, to address the complexity and scale of agentic AI systems.
  • Smartscape, a dynamic, real-time topology discovery, maps causal dependencies across complex digital ecosystems, enhancing the platform’s ability to deliver precise insights and automation, fueling Dynatrace AI for fact-based reasoning.
  • Davis AI combines causal, predictive, and generative AI techniques to analyze unified data, deliver trustworthy insights, and intelligently plan actions to minimize hallucinations, maximize precision in insights and answers, and adhere to responsible AI practices and compliance requirements.
  • AutomationEngine executes autonomous tasks and integrates seamlessly with first-party and third-party AI agents, driving differentiated efficiency and adaptability, all in a safe and privacy-protected manner for controlled agentic AI actions.

Through sustained innovation, Dynatrace provides the intelligence and autonomy developers, business leaders, and organizations need to tackle digital complexity and achieve extraordinary outcomes.

“These innovations provide the transparency, automation, and agility enterprises need to stay ahead in a rapidly evolving technology landscape,” Greifeneder added. “The result is a platform capable of autonomously preventing potential issues, optimizing resource use, and adapting to unforeseen challenges. Agentic AI represents a fundamental transformation in enterprise technology. Our platform not only delivers insights but actively enables businesses to anticipate challenges, adapt to changing conditions, and achieve their boldest ambitions. This evolution embodies our long-standing vision to empower organizations through trustworthy automation and actionable intelligence.”

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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 ...

Dynatrace Adds Agentic AI Capabilities

Dynatrace is extending the Dynatrace platform with agentic AI capabilities. 

Designed to predict and prevent disruptions, protect systems and data, and optimize operations autonomously, these advancements mark a new era of productivity and agility, fundamentally redefining how businesses manage digital transformation.

Dynatrace leverages agentic AI to enabling enterprises to transition from manual oversight to autonomous, AI-driven workflows that streamline operations and foster innovation.

“We anticipated the growing complexity of digital systems outpacing the capabilities of traditional observability solutions reliant on human intervention,” said Bernd Greifeneder, Founder and CTO, Dynatrace. “This is why we built the next generation of our platform to help customers leverage advanced AI to offload work and unlock entirely new possibilities. By unifying observability, security, and business data in a revolutionary data lakehouse architecture, we’ve created the foundation for AI to deliver real-time insights and act autonomously in ways that were unimaginable a few years ago.”

The core of Dynatrace is a purpose-built foundation for agentic AI, seamlessly unifying observability, security, and business data in a schema-free, indexless data lakehouse. This foundation empowers intelligent decision-making and autonomous action, enabling enterprises to transition from human oversight to intelligent, self-operating systems. Together, the platform serves as the knowledge, reasoning, planning, and actioning framework of agentic AI, delivering trustworthy precision and adaptability.

  • Grail, an indexless, schema-free data lakehouse, hot/hot by design, provides real-time access to petabytes of data in context, eliminating the inefficiencies of re-indexing and rehydration, to address the complexity and scale of agentic AI systems.
  • Smartscape, a dynamic, real-time topology discovery, maps causal dependencies across complex digital ecosystems, enhancing the platform’s ability to deliver precise insights and automation, fueling Dynatrace AI for fact-based reasoning.
  • Davis AI combines causal, predictive, and generative AI techniques to analyze unified data, deliver trustworthy insights, and intelligently plan actions to minimize hallucinations, maximize precision in insights and answers, and adhere to responsible AI practices and compliance requirements.
  • AutomationEngine executes autonomous tasks and integrates seamlessly with first-party and third-party AI agents, driving differentiated efficiency and adaptability, all in a safe and privacy-protected manner for controlled agentic AI actions.

Through sustained innovation, Dynatrace provides the intelligence and autonomy developers, business leaders, and organizations need to tackle digital complexity and achieve extraordinary outcomes.

“These innovations provide the transparency, automation, and agility enterprises need to stay ahead in a rapidly evolving technology landscape,” Greifeneder added. “The result is a platform capable of autonomously preventing potential issues, optimizing resource use, and adapting to unforeseen challenges. Agentic AI represents a fundamental transformation in enterprise technology. Our platform not only delivers insights but actively enables businesses to anticipate challenges, adapt to changing conditions, and achieve their boldest ambitions. This evolution embodies our long-standing vision to empower organizations through trustworthy automation and actionable intelligence.”

The Latest

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 ...