Turning run history into safe learnings
Run history becomes useful only after filtering, grading, scoping, and consolidation.
Raw run history contains useful signals, accidental noise, user-specific preferences, and failed experiments. Safe learning separates those categories.
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
- Extract candidate lessons from successful and failed runs.
- Attach the evidence span or artifact.
- Scope each lesson to product, repo, tenant, or team.
- Consolidate duplicates before retrieval.
Implementation checklist
- Keep raw history immutable.
- Require reviewer approval for broad lessons.
- Record supersession when a lesson changes.
- Measure whether retrieved lessons improve future grades.
Watch outs
- Do not retrieve every historical note.
- Do not promote private user preferences globally.
- Do not mix failed attempts with accepted operating rules.
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.