Regression testing self-improving agents
5 Jul 2026 · 7 min read
Every accepted lesson should prove it improves future runs without breaking known-good behavior.
Self-improvement needs a regression harness. Without one, the system cannot distinguish learning from drift.
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
- Maintain representative tasks for each agent role.
- Replay old runs with new prompts or lessons.
- Compare artifacts, grades, costs, and latency.
- Promote changes only when the improvement clears a threshold.
Implementation checklist
- Build a golden task set.
- Store expected acceptance criteria.
- Run before and after comparisons.
- Block promotion on quality or cost regression.
Watch outs
- Small eval sets overfit quickly.
- Cost regressions often hide behind quality gains.
- Lessons learned from one repo may hurt another.
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.