AI Observability in Grafana Cloud: A complete solution for monitoring your agentic workloads

The observability industry has developed great tools for using metrics, logs, traces, and profiles to monitor the cloud native applications that have dominated the last decade of software development. 

But when it comes to understanding what an AI system is actually doing, we’re often left reading raw conversations, guessing at quality, and reacting too late. And that’s a problem. 

Agents make decisions, call tools, generate content, and interact with users, services, and applications in ways that traditional observability isn't designed to handle. As organizations shift from cloud native to AI native, it's increasingly clear that agent chats and sessions need to be treated as first-class signals alongside the rest of your more traditional telemetry. 

To address this emerging gap, we're launching AI Observability in Grafana Cloud. Available now in public preview, AI Observability actually started as an internal hackathon project designed to address some of our own agentic challenges. Since then, we've heard from lots of customers dealing with similar problems, so we decided to take what we learned and turn it into a complete solution for teams running agents in production, helping them understand what their AI is doing, how well it’s doing it, and where issues are emerging.

How AI Observability can help you today

Traditional observability gives you signals like CPU usage, request latency, and error rates. Those are important. But they don’t tell you if your agent is being helpful, hallucinating, or quietly degrading over time.

That's why we've built AI Observability in Grafana Cloud to help teams:

• Observe AI agent behavior in real time, including inputs, outputs, and execution flows
• Continuously evaluate outputs, with alerts for issues such as low-quality responses, policy violations, or anomalous behavior
• Surface risk earlier, including potential data exposure or misuse (for example, leaked credentials or abnormal usage patterns)
• Elevate agent sessions and conversations to first-class telemetry signals and correlate them in the same environment where applications are observed

AI Observability connects agents directly to traces, tool calls, token usage, costs, and (live) evaluations. And it does it all in the same Grafana Cloud environment where you observe the rest of your systems. 

This gives you true end-to-end signals—agentic or otherwise. So the next time something looks off, you won't just see a spike in latency. You'll also be able to open the exact conversation, inspect what happened, and understand why.

Instrument once, understand everything with open standards

AI Observability is OpenTelemetry-compatible, so it fits naturally into existing observability setups. You instrument your app once using a thin SDK, and AI Observability automatically captures:

• Generations and conversations
• Model and provider metadata
• Tool usage
• Latency and token metrics
• Cost signals

From there, everything becomes queryable and explorable in one place. You can also filter by model or provider, time range, labels, or environments. This is particularly helpful if you have different providers, where the same model can function differently in different environments.

AI Observability automatically classifies and catalogs agent versions for you. If you change an agent’s system prompt or its tool set, a new agent version is created that you can inspect separately. This helps you find the best performing agent you have and spot specific problems agents might have.

Agents can break subtly; AI Observability gives you the context to know why

One of the hardest parts of running AI in production is that issues are often subtle. Nothing crashes. No alerts fire. But something is off and your users complain. Responses are getting longer and less useful, costs are creeping up, quality is slowly degrading, users are losing trust.

AI Observability is designed for this exact problem. You can drill into any conversation and see the full thread: tool calls and execution traces, token usage and cost breakdown, scores, ratings, and annotations. 

This is critical for debugging your agents. It helps you understand if specific agents struggle with specific models, or the impacts of your latest release. You can also know where your tokens are going—and, as a result, where your money is going. You can see if certain operations are expensive, or if certain tools are slow or struggle with certain tasks, which in turn adds to your costs.  

And if you want additional debugging support or advice for how to improve your agents, you can ask for help from Grafana Assistant using natural language. Because it can correlate your AI data with all your other telemetry signals, it can help all sorts of use cases. 

For example, it can see how much time you're spending on compute, or what's causing spikes in latency, and you can find out how that ties back to your AI. Essentially, it takes the power of our existing full-stack observability platform and extends it to the next-generation of applications your business relies on.

Get alerts when they matter

AI Observability can also help assess your AI's accuracy, which can become a major challenge when you have multiple agents running at scale. 

You can use LLM-as-a-judge, heuristics, or regex to detect undesirable outputs. And because AI Observability natively integrates with Grafana Alerting, you can get notified when your agents behave out of control. Taken collectively, this allows you to treat agents like the rest of your services and infrastructure.

You can even combine this with Assistant skills and agent-specific runbooks. That way, when you get paged about increased toxicity from your agent, you can ask Assistant to inspect the conversation, read your runbook, and offer remediation strategies.

AI Observability, from the team that built Grafana Assistant

We know about the challenge of observing agents because we've lived through it. When we started building Assistant, we looked at lots of frameworks, but they just weren’t detailed enough for how we wanted to monitor our agents.

We quickly realized we needed to build our own in-house solution. Assistant has been very well received by our users—so popular, in fact, that we're making it available in new and exciting ways—and a big part of that success has been our ability to closely monitor the feedback loop we built so we can keep track of how performance and customer behavior evolves. 

That success led us to think we should make this available to our customers. So, during a recent hackathon, the Assistant team took what it learned from monitoring agents, prompt engineering, keeping track of agent versions, handling tools, and more, and baked it into a solution that enables you to monitor agents at a large scale, too. 

As with Assistant, AI Observability is at the center of continuous innovation. We've already shipped new features, including user annotations and streamlined alerting, after working with customers during the private preview. And we're excited to continue to innovate as we open the solution to a wider audience. 

Starting today, you can find AI Observability in Grafana Cloud and start using it right away. Start with the demo mode to get a feel for how it works with some example data. And when you’re ready, you can hook up your own agents and start analyzing them.

For more information on this and all the other exciting updates from GrafanaCON 2026, check out our announcement blog for all the news. And for more information on Grafana Cloud AI, including FAQs about Assistant and our other AI capabilities, check out our AI observability page.

Grafana Cloud is the easiest way to get started with metrics, logs, traces, dashboards, and more. We have a generous forever-free tier and plans for every use case. Sign up for free now!