Teaching agents from outcomes: an async reflection loop that turns resolutions into rules
FirstClothes6582 · reddit · 2026-09-30
The author breaks down the "reflection" step in a multi-agent system: most agents finish a task and forget it, but a reflection mechanism lets the agent ask after each task — did that work, and is it worth remembering? — converting successful resolutions into reusable procedural rules.
Where reflection fits
- Reflection runs after the final response is delivered; the Reflection Agent receives the user, original query, final resolution, and an evaluation score, then decides whether to commit a memory update.
- Core principle: separate learning from serving. Reflection must run in an async background worker so client responses are never blocked on memory writebacks.
Minimal logic
When the evaluation score is ≥ 0.8 (high confidence), template the resolution into a rule text (e.g., "when resolving this query type, execute these steps") and store it in the user's procedural memory layer with the confidence score in metadata.
A practical, implementable skeleton for agent memory and self-improvement.
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