Dynatrace, Google Cloud Push Observability Into The Agentic AI Era

As enterprises move from experimenting with AI to running it inside production environments, a familiar constraint is resurfacing: visibility. Without deep, real-time insight into how systems behave, autonomous AI agents risk becoming black boxes rather than business accelerators.

Dynatrace’s expanded collaboration with Google Cloud signals how that problem is beginning to be addressed.

Announced on December 22, the observability platform has become a launch partner for Gemini Enterprise and Gemini CLI extensions, Google Cloud’s new agentic AI capabilities, positioning observability closer to where AI systems are built, deployed, and operated.

For enterprises betting on agentic AI, the message is clear: autonomy without oversight does not scale.

Observability Moves Closer To The Developer Terminal

One of the most immediate changes comes at the developer level.

The Dynatrace Gemini CLI Extension brings observability and root-cause analysis directly into the command line. Developers can monitor, debug, and optimise applications without switching tools, an approach aligned with how modern DevOps teams already work.

In practice, this reduces friction during development cycles, where delays often stem not from code issues but from diagnosing what changed, where, and why. By embedding observability inside the terminal, Dynatrace is effectively treating system visibility as a first-class development primitive rather than an afterthought.

Agent-to-Agent Communication Enters Enterprise Operations

At the platform level, the partnership goes further.

Through Google Cloud’s Agent-to-Agent (A2A) protocol, Dynatrace integrates directly with Gemini Enterprise, allowing AI agents to exchange operational intelligence in real time. The result is a system where detection, analysis, and response can happen collaboratively across AI agents rather than in isolated silos.

For large enterprises running hybrid and multi-cloud environments, this matters. As AI agents begin managing workflows across infrastructure, applications, and services, the ability for those agents to share context becomes critical to preventing cascading failures.

Marketplace Distribution Targets Faster Enterprise Adoption

To reduce deployment complexity, Dynatrace has also made its AI-driven integrations available via Google Cloud Marketplace.

This allows enterprises to deploy and scale observability-enabled agentic AI using existing procurement and security frameworks, an often-overlooked factor in enterprise adoption. For CIOs and platform teams, the marketplace route shortens the path from proof-of-concept to production.

Dynatrace is also among the first observability vendors with a Google-validated A2A and Gemini Enterprise-compatible agent, placing it inside Google Cloud’s validated partner ecosystem.

Why Observability Is Becoming A Strategic AI Layer

The broader implication is less about tooling and more about architecture.

As AI agents take on greater responsibility from remediation to optimisation, enterprises are discovering that traditional monitoring models are insufficient. Observability, in this context, becomes the control plane that keeps autonomous systems accountable, explainable, and resilient.

Jay Snyder, SVP of Partners and Alliances, Dynatrace, said, “Our expanded partnership with Google Cloud reflects Dynatrace’s leadership position in observability and our commitment to shaping how AI transforms cloud operations. As a launch collaborator for both Gemini Enterprise and Gemini CLI extensions, we’re working with Google Cloud at the forefront of AI and observability innovation, helping customers build intelligent, reliable systems that adapt and optimise in real time.”

Industry analysts see the same shift playing out across enterprise AI programmes.

Mitch Ashley, VP and Practice Lead, Futurum Research, said, "Accelerating AI across the enterprise requires a visibility that connects developer innovation directly with operational resilience. Dynatrace's Gemini CLI Extension, combined with Dynatrace's Agent-to-Agent (A2A) integration into Gemini Enterprise, removes friction and increases velocity for operations to keep the enterprise in the flow of utilising agentic AI as a scalable core business driver."

What Dynatrace and Google Cloud are signalling is a transition point.

Agentic AI is no longer confined to research environments or narrow use cases. It is moving into the operational core of the enterprise, where uptime, performance, and accountability matter as much as innovation.

In that world, observability is no longer a support function. It becomes the connective tissue between autonomous systems and human oversight.

For enterprises planning their next phase of AI adoption, the takeaway is straightforward: the success of agentic AI will depend not just on smarter models but on how clearly organisations can see and control what those models are doing in real time.