Self-Improving + Proactive Agent

An agent learns from mistakes by logging explicit user corrections and preserving confirmed preferences in local memory files. It organizes reusable lessons into hot, warm, and cold tiers, ensuring execution quality compounds over time instead of treating tasks as isolated events.

The best way to organize agent memory is using a tiered management system that categorizes knowledge into hot, warm, and cold storage. This approach applies promotion and demotion rules to keep active data accessible while archiving older lessons.

You capture user corrections through correction logging, which automatically routes explicit feedback to the appropriate local memory file. This prevents repeated errors by ensuring the agent records and applies the feedback to future responses.

Yes, you need local file-based storage under the ~/self-improving/ directory to maintain agent state. Heartbeat maintenance uses recurring checks to keep these local memory files tidy without destructive churn, relying on conservative update rules.

An agent should trigger proactive self-reflection after significant work is completed. It reviews the finished task, identifies what could be improved, and records reusable lessons to ensure future responses avoid repeated errors and maintain high quality.

The limitations of conservative update rules include avoiding any deletion of existing memory and strictly not inferring preferences from silence. This cautious approach prevents destructive churn but may require explicit user corrections to update the agent's state.