Self-Improving + Proactive Agent

To make an AI agent learn from corrections, you need a self-improving mechanism that logs user feedback and performance signals into a local memory. This allows the agent to refine its behavior and reduce repeat mistakes across future tasks within the workspace.

A heartbeat in proactive AI agents is a continuous background process that applies heartbeat rules to anticipate next steps, verify outcomes, and stabilize improvements. It enables self-reflection and proactive behavior across ongoing workspace tasks.

To log corrections and build memory, load the local memory template file and begin recording user feedback. This creates a structured HOT, WARM, and COLD memory tier system that compounds over time to improve future outputs.

Yes, you can use local memory for AI agents without external exposure. This self-improving approach uses local storage for memory templates and corrections logs, requires no external network by default, and enforces strict security boundaries around your data.

The best way to prevent an AI agent from repeating mistakes is implementing a self-criticism mechanism that logs corrections into a structured local memory. This creates compounding performance gains by reflecting on past outcomes and refining future behavior.

No, a self-improving agent does not require external network access to function. It operates entirely offline using local storage for memory templates, corrections logs, and heartbeat rules, ensuring sensitive workspace data remains secure.