continuous-learning

AI agents learn from past executions by using Critic, Reflection, and Skill Synthesizer agents to analyze performance, identify recurring patterns and anti-patterns, and automatically propose new skills for team approval.

You can automate Kubernetes error resolution by deploying a continuous learning system that analyzes successful agent executions handling specific errors and proposes targeted new skills with usage examples and confidence scores for deployment.

Continuous learning for AI agents uses a Discord approval workflow to facilitate human-in-the-loop review, ensuring teams validate and approve proposed skills generated from identified patterns before they are deployed.

The best way to store and query agent learnings is by integrating the continuous learning system with memory systems, which retain execution patterns and anti-patterns for future querying and skill generation.

You can trigger continuous learning cycles and manage the system via CLI commands, which allow you to initiate the process of evaluating recent agent executions and synthesizing new skills directly from the terminal.