Observability: no invisible autonomous changes

What is an agent action trace?

Think of it as a distributed trace whose spans are reasoning steps. A single trace joins: the triggering intent or event; the context the agent saw; the plan it formed; the policy evaluations (pass and fail) from governance; the identity under which it acted; the exact mutations executed; and the measured outcome. When something breaks at 3 a.m., the question "why did the agent do that?" must be answered by reading a trace — not by interviewing whoever wrote the prompt.

How is this different from LLM logging?

• →Joined, not scattered. Chat transcripts, policy logs, and infra events share correlation keys; the story assembles itself.
• →Structured decisions. Traces record decision artifacts — proposed change, rationale, expected effects — not prose to be re-interpreted.
• →Outcome-linked. Each action spans into service telemetry, so cause and effect connect across the agent/system boundary.
• →Standards-shaped. OpenTelemetry's GenAI semantic conventions are the emerging vocabulary; the plane adopts rather than invents.

How do you observe behavior over time, not just single actions?

Traces answer "what happened here." Evals answer "is the platform's judgment any good?" — policy rejection rates over time, human override rates per action class, mean remediation attempts before compliance, escalation correctness, rollback frequency. These are the metrics an agentic platform is actually managed by (we list them in the demo case study), and they only exist if the observability plane records the underlying events in queryable form.

What happens without it?

Agents become unmanageable in the specific sense that incidents become unattributable: changes appear with no author, fixes happen with no record of reasoning, and trust decays with every "we think the agent did that." Observability is also the enabling plane for progressive autonomy — you can only raise an agent's autonomy on evidence, and evidence is what this plane produces.