Self-Improving + Proactive Agent

To make an agent learn from corrections, log explicit user feedback to a local corrections file, track repeat counts, and promote recurring lessons to persistent memory after user confirmation. This ensures stable patterns improve future behavior.

Tiered local memory organizes context into HOT, WARM, and COLD files under a local directory with project and domain namespaces. This loads only relevant context, minimizes token bloat, and structures agent experience for retrieval.

Safe memory maintenance uses heartbeat-driven rules to update indexes and compact oversized files conservatively. This preserves data integrity without destructive rewrites, ensuring persistent memory remains reliable over time.

Yes, local storage in a dedicated directory is required. The self-improving agent needs this local file system to log corrections, maintain tiered memory, and run heartbeat maintenance routines safely.

File-based memory prevents context bloat by structuring data into HOT, WARM, and COLD tiers with project namespaces. The agent loads only relevant context files, keeping the active prompt concise while retaining long-term lessons.

Avoid this approach if your environment lacks local storage access or requires instant responses without heartbeat maintenance. The system needs explicit user confirmation to promote global preferences, which adds latency to workflows.