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Loading.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).
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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.
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.
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.
or have us build it — same capability, the other door