Self-Improving Agent (With Self-Reflection)

To make an agent learn permanently, you capture user corrections and self-reflection into durable tiered memory. This skill logs mistakes, promotes repeated corrections into preferences, and applies them consistently in future sessions using local file-based storage.

Self-reflection improves agent workflows by evaluating whether output met intent after significant work. The mechanism identifies concrete improvements and logs them into tiered local memory, ensuring outcomes get better without manual maintenance across multi-step tasks.

The best way to stop repeated mistakes is logging explicit user corrections and tracking issues for promotion into durable preferences. This skill records corrections in local file-based memory, applying conflict resolution and safety boundaries to prevent recurrence.

Yes, local file storage is required for agent memory and preference learning. This skill requires a dedicated directory under ~/self-improving/ to store confirmed preferences, active patterns, and log rules for per-project and per-domain learning.

Yes, you can apply self-improving agent memory for per-project context isolation. The tiered local memory supports per-project and per-domain learning, storing confirmed preferences in dedicated files to ensure context-specific improvements are maintained separately.

Limitations of local file-based memory for agent self-improvement include requiring local storage capacity under ~/self-improving/ and enforcing safety boundaries. Memory compaction and conflict resolution rules are needed to manage growing logs and prevent contradictory preferences.