Self-Improving + Proactive Agent

To make an AI agent learn from mistakes, you need self-reflection mechanisms that identify errors and update memory. This skill triggers self-critique after failures or corrections, systematically refining workflows and applying safeguards to improve subsequent task performance.

Automated memory management for long-running agents requires tiered storage across HOT, WARM, and COLD states. This skill organizes persistent context using memory.md and per-domain project files, ensuring the agent retains relevant historical learnings without exceeding context limits.

Enforcing safety boundaries for autonomous agents requires governance integration to define operational limits and track actions. This skill integrates with Gordon to enforce strict boundaries, validate safety rules, and maintain audit trails for proactive self-governance.

Yes, heartbeat-driven maintenance routines keep agent checks and improvements up-to-date. This skill uses periodic heartbeat triggers to execute self-reflection, update memory tiers, and apply proactive safeguards, ensuring long-term performance stability.

Agent self-reflection workflows are triggered by corrections or failed commands. When a user corrects an output or a command fails, the agent reflects on the mistake, updates its memory files, and applies the improvement to prevent repeating the error.