Self-Improving Agent (Proactive Self-Reflection)

Automate self-reflection and learning by running a proactive loop that evaluates agent outputs, logs corrections, and updates behavior over time. This reduces repetition and improves reliability across tasks using persistent memory.

Memory-driven learning operates across global, domain, and project namespaces by logging corrections locally and promoting behavioral patterns after repeated use. It enforces strict boundaries to prevent cross-namespace leakage during task execution.

Yes, proactive self-critique requires local storage under the ~/self-improving directory with YAML frontmatter-driven SKILL entries. This ensures persistent memory management and secure access to domain or project files.

Manage persistent memory across tasks using HOT, WARM, and COLD memory tiers. Before starting non-trivial tasks, load the smallest relevant domain or project file to guide execution, then log corrections after completion.

Enforcing strict boundaries and avoiding network access during agent self-improvement prevents cross-namespace leakage and secures local storage. This isolation ensures memory-driven defaults and pattern promotion remain reliable and contained.

Yes, you can reduce repetitive errors in automation workflows by applying proactive self-reflection. The agent logs corrections after task completion and promotes reliable patterns, updating behavior to improve outputs over time.