reflect

To make agent improvements persist across sessions, you can encode learnings from conversations into agent files. Reflect detects user corrections and categorizes them for permanent updates.

Extracting actionable learnings involves applying signal detection to identify corrections during conversations. You can review discussions to automatically categorize and propose updates to targeted agent files.

Human-in-the-loop approval for self-improvement requires explicit consent before applying changes. The system proposes updates and new skills, but you must manually approve modifications to ensure safety and maintain traceability.

You can rollback agent behavior changes using versioned guardrails. The system maintains versioning and logging for all proposed updates, ensuring you can safely revert modifications if they cause issues.

Signal detection for agent feedback does not require manual setup to function. It automatically identifies corrections with confidence levels during conversations, categorizing them to targeted agent files.

You should not use automated agent feedback encoding if your workflow lacks a human-in-the-loop approval step. The system requires explicit human approval to maintain safety and ensure changes are traceable.