Hi all,
I’m working on MUTX, an open-source control plane for operating AI agents in production environments. The project focuses on the operational side of agent systems: identity, deployment lifecycle, webhook orchestration, API key governance, and observability across CLI/API/SDK/dashboard surfaces.
While exploring observability patterns for agent workloads, I realized that the Grafana stack maps surprisingly well to the problem.
An agent run can be thought of as a structured workflow:
- a top-level agent execution
- nested tool calls
- model inference steps
- external API calls
- intermediate reasoning and state transitions
Conceptually this feels very close to distributed systems tracing.
One architecture we’re experimenting with is:
- Tempo → traces representing full agent runs and tool-call spans
- Loki → logs emitted by agents and tools during execution
- Prometheus → metrics like task latency, failure rates, token usage, and queue depth
- Grafana dashboards → operational views of agent health and workload behavior
The control plane would emit structured telemetry for each agent run so that an operator could inspect:
- full execution traces
- tool latency and failure patterns
- model call performance
- agent-level SLOs
I’m curious whether anyone in the community has experimented with similar patterns for long-running LLM agents or autonomous workflows, and whether there are best practices for modeling these kinds of systems in the Grafana stack.
If this is an interesting direction, I’d also be curious whether it might make sense to build a small integration layer or example project showing how agent workflows map into Tempo/Loki/Prometheus telemetry.
Thanks!
Mario
Summary
TL;DR: I’m building an open-source control plane for AI agents and exploring how agent runs could map into the Grafana stack (Tempo for traces, Loki for logs, Prometheus for metrics). Curious if anyone here has experimented with observability patterns for LLM/agent workflows.