Agent Observability
Overview
Agent Observability is built on OpenTelemetry and gives teams running LLM agents in production a single place to monitor agent activity, trace conversations, track costs, and evaluate quality.
Agent Observability provides thin SDKs for Go, Python, TypeScript, Java, and .NET that capture generation data with minimal code changes. Built-in framework integrations for LangChain, LangGraph, OpenAI Agents, Vercel AI SDK, and others make instrumentation automatic.
With Agent Observability in Grafana Cloud, you can browse conversations, drill into traces, compare agent versions, configure online evaluation rules and guards, review offline experiment reports, and use built-in analytics for metrics, logs, traces, and profiles.
Use Agent Observability
Use Agent Observability to understand agent behavior from development through production. You can:
- Capture LLM generations, tool calls, workflow steps, token usage, and cost data from your agents.
- Forward coding-agent sessions from supported tools without adding SDK code to an application.
- Search and inspect conversations, then connect individual generations to OpenTelemetry traces and metrics.
- Track agent versions, prompt changes, tool usage, quality scores, latency, errors, and cost trends.
- Configure online evaluations and guards to score or gate production traffic.
- Run offline experiments to compare models, prompts, and agent versions before release.
Explore
Frequently asked questions
What is Grafana Agent Observability?
Grafana Agent Observability provides end-to-end tracing and monitoring for AI agents, allowing you to track user requests through planning, tool invocations, LLM calls, and final responses.
What does Grafana Agent Observability track?
It tracks performance metrics like end-to-end latency, token usage, and API costs, while also capturing agent reasoning paths, tool usage, and execution flow to help debug and optimize agents.
Why is observability important for AI agents?
Because AI agents are non-deterministic, observability is crucial for understanding their reasoning paths, tracking tool invocations, managing costs, and troubleshooting behavioral issues when they produce incorrect answers.
