Observe and recover AI agents: control in the workflow

The practical problem

An agent can report “done” when tests are missing, scope drifted or a dependency was never met. Without visible state, a retry becomes guesswork and a release decision becomes intuition.

Questions visible state can answer

An owner should be able to see whether a task is ready, who owns it, which dependency is missing, what was checked and what needs deciding next. This separates a documented result state from a chat summary.

• Claim and lease to avoid duplicated concurrent work
• Result and effect recording instead of only “done”
• QA state and known open points
• Pause and stop reasons before a risky next step

Retry and recovery with control

Repeating work makes sense only when the reason is visible: transient failure, missing input, bad scope or missing approval. AI Operator Bridge and the workflows can prepare task and report context for a controlled continuation. They do not guarantee a successful later run.

Safe boundaries

Public observability must not expose secrets, internal security mechanics or private repositories. This page therefore describes concepts and decision artefacts, not sensitive implementation details.