Agent lessons, decisions, and rollback
2 Jul 2026 · 6 min read
Agent learning systems need rollback just like code, because bad lessons can affect many future runs.
A lesson is an operational change. It should have provenance, rollout scope, monitoring, and rollback.
AstraRunner treats this as an execution-platform concern: the run needs durable state, traceable decisions, cost visibility, and enough structure for teams to review what happened after the agent finishes.
Patterns that work
- Store lessons as versioned records.
- Link each lesson to the decision that accepted it.
- Roll out lessons gradually by team or workflow.
- Compare grade trends after activation.
Implementation checklist
- Record author, approver, and evidence.
- Add activation and expiration dates.
- Support disable without deleting history.
- Surface active lessons in run traces.
Watch outs
- Silent lesson edits destroy auditability.
- Global rollout from one incident is risky.
- Expired lessons can keep teaching obsolete behavior.
Related reading
How this fits the Astra stack
AstraRunner owns the operational path for agent work: orchestration, scheduling, approval gates, traces, costs, and role-agent handoffs. AstraMemory owns durable context and retrieval. AstraGenie turns those capabilities into product workflows for teams that want automation without rebuilding the platform layer.