What is agent observability?

Agent observability is seeing what your AI agents actually do at runtime, every step, tool call, input/output, token cost, and failure mode, across a fleet, so you can debug a run, control cost, and catch silent wrong-answers. It's the agent-shaped version of tracing: the unit is the non-deterministic run, not a deterministic request. yoru is an open-source, self-host take on it (public beta).

Updated 19 June 2026

The definition, plainly

An agent decides its own steps, so you can't predict its path. Observability is the ability to reconstruct any run after the fact, what it read, which tools it called, what it returned, what it cost, and to watch the fleet for cost and failure trends.

Why agents specifically need it

Three reasons: failures are usually silent (a wrong answer, not a crash); cost is variable and compounds across a fleet; and the path is non-deterministic, so without a trace you can't tell why a run went wrong. Logs alone don't cut it, you need the structured run.

The honest yoru note

yoru is the open-source, self-host observability pillar of a self-host suite, run it yourself, public beta, in active development. The point of this page is the concept; yoru is one way to put it in practice on your own infra.

Straight answers.

How is agent observability different from logging?: Logs are flat lines; observability is the structured run, the trace of steps, tool calls, cost, and outcome you can replay and aggregate across a fleet.
What's the hardest agent failure to catch?: The silent wrong-answer: a plausible, well-formed output that's incorrect. It throws no error; you only catch it by observing outcomes against a verification step.
Does agent observability replace evals?: No, they pair. Evals test before deploy; observability watches what actually happens in production.
Is yoru a hosted service?: No. Open source, self-host, you run it. (Public beta.)

Keep reading.