continuous-learning

To automatically improve agent prompts from past session logs, this skill mines successful execution traces for statistically significant patterns and converts them into validated, versioned prompt refinements.

Continuous learning for autonomous agents is the process of automatically extracting successful session patterns, validating them against held-out data, and codifying them into reusable skills to improve accuracy over time.

You can generate agent skills from telemetry data by logging outcome-annotated execution traces, applying pattern mining with significance thresholds, and automatically converting the results into structured skill templates.

Yes, automated skill generation requires A/B testing before deployment. Candidate skills are tested on held-out sessions and promoted only after A/B testing shows measurable improvement.

Use pattern mining for repetitive agent workflows when tool sequences, prompt changes, and error-recovery strategies produce measurable outcomes across many sessions that need to be codified into reusable skills.