What Incident Reviews Need From AI Logs

A good incident review reconstructs what happened from durable evidence. A bad one reconstructs it from memory and Slack scrollback. The difference is decided long before the incident, in how the system structures its logs — and most AI systems log for debugging, not for review.

Logs vs. traces

Conventional application logs are a stream of strings written for whoever is tailing them at the time. A trace is a structured, causal record written for whoever opens it months later. For AI workflows the review needs the second kind: events that connect query to retrieval to decision to action to outcome, each with enough context to stand alone.

In practice an incident review needs to answer five questions from artifacts alone, with no re-running of the model: what context was retrieved, what the model produced, which policy decisions were evaluated, which tools were called, and where a human overrode the system.

Structure that survives the handoff

The reviewer is rarely the person who built the workflow. That means the trace has to carry its own context: source metadata on retrieved records, the routing weights behind a profile selection, the inputs a policy gate evaluated, and the identity and timestamp on every human intervention. A trace event that only makes sense to its author is a trace event that fails the review.

Private does not mean unaudited

Teams running offline or in a VPC sometimes assume that audit is something you trade away for privacy. It is the opposite. Local audit trails, release gates, and repeatable evals are exactly what make a private deployment defensible. The trace stays inside your boundary; its rigor does not have to.