Dynatrace and Arize AI Push Observability From Detection Toward Action

In the podcast, Paul Nashawaty, practice lead and principal analyst at theCUBE Research, spoke with Steve Tack, chief product officer of Dynatrace, and Aparna Dhinakaran, co-founder and chief product officer of Arize AI. They discussed why application observability and AI observability are converging and what that means for enterprise operations.

Traditional observability platforms were built around deterministic software and telemetry such as logs, metrics and traces. AI applications and agents behave differently, producing outputs that can vary even when given similar inputs. At the same time, enterprises increasingly expect observability platforms to move beyond identifying problems and provide enough context for humans and AI agents to diagnose, remediate and potentially act on those problems.

Tack said the world has shifted significantly and that AI brings new problems and new domains to the space. Dhinakaran said evaluation is no longer just about whether an answer is right or wrong. “It becomes about actually measuring the quality of the responses, which is just a very fundamentally different problem,” she said.

Arize built its platform around that challenge, providing tools for tracing, evaluating and improving AI applications and agents. Its open-source Phoenix platform is used by more than 4,000 enterprises, according to Dhinakaran, while Arize AX provides a managed environment designed for teams operating AI systems at production scale. For Dynatrace, those capabilities expand observability into an application layer that is becoming more important as enterprises move AI projects from experimentation into production.

AI applications rarely operate independently. Agents call application programming interfaces, interact with databases, depend on cloud infrastructure and connect to broader enterprise systems. Dhinakaran said Arize customers increasingly wanted stronger connections between AI telemetry and traditional application and production telemetry. Dynatrace customers, meanwhile, were asking for deeper AI observability and evaluation capabilities. Bringing those environments together could give developers, site reliability engineers, platform teams, AI engineers and data scientists a shared view of what is happening across the application stack.

“The agent systems and the software systems are joined at the hip,” Dhinakaran said. “Having this ability to not only debug agents with AI observability, but also have all the context of the software that they use to call tools or the underlying infra behind the agents … just makes us build better products.”

That shared context could also help address another persistent observability problem: tool sprawl. According to research cited by Nashawaty during the conversation, 75% of organizations use between six and 15 tools for observability. As enterprises add AI monitoring, evaluation and governance systems, the risk is that AI creates another isolated operational layer rather than reducing that complexity. Tack argued that combining application and AI observability gives organizations a more complete system-level view rather than forcing teams to piece together information across disconnected platforms.

“The real loss often happens [when] they lose the ability to have a system mindset,” Tack said. “How can we bring a broader view together? How can we have shared context? How can we take action?”

The discussion framed the broader shift as observability moving from detection toward action, with AI both creating new troubleshooting challenges and becoming part of the response. The executives pointed to shared context across AI and application telemetry as a way to help teams understand and act on problems that span models, agents, software and infrastructure.