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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.”

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.

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

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.