Agent self-learning needs graders before feedback loops
Self-improving agents only work when run history, rubrics, and regression gates stop bad lessons from compounding.
Self-learning is not magic memory. It is a controlled change process where proposed lessons need evidence, scope, 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
- Capture task, inputs, tool calls, outputs, and final artifact.
- Grade the result before saving a lesson.
- Store accepted learnings separately from raw observations.
- Run regression checks before applying lessons globally.
Implementation checklist
- Define the rubric before the run.
- Record grader version and model.
- Require evidence links for accepted lessons.
- Expire or supersede stale lessons.
Watch outs
- Reflection without grading becomes accidental policy.
- A single failure should not mutate global behavior.
- Lessons need tenant and application scope.
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.