self-improving-agent

An agent learns from mistakes by automatically logging failures, corrections, and missing capabilities to build a learning memory. It then aggregates these learnings to adjust future responses through memory ranking and pattern adaptation across sessions.

Automatic experiential feedback logging is the process of capturing errors and corrections to enable continuous improvement. It aligns memory with user feedback and failures, organizing structured learnings and recovery suggestions for the agent.

To log a correction and display recent learnings, ask the agent to log the correction. The system will automatically capture the experiential feedback, organize it into structured learnings, and display the most recent entries.

Yes, you can use aggregated learnings to adjust memory ranking across sessions. The system captures experiential feedback from failures and applies these learnings to guide memory ranking and optimize future performance.

Yes, self-improvement memory works with QAVR integration to improve retrieval and decision making. It integrates structured learnings from failures and corrections to enhance the retrieval process based on learned experiences.

The limitation of relying on failure logging for capability tracking is that it depends on encountering failures, corrections, and missing capabilities to build its learning memory, requiring continuous experiential feedback to optimize performance.