verify-learning

Agent learning validation works by evaluating specific behavioral sections for PASS, FAIL, or N/A status. It examines evidence from pipeline records, journal entries, and reasoning banks to confirm the agent correctly implemented new behaviors and is properly prioritizing, deferring, and tracking goals.

Validate agent behavior by walking through a predefined verification checklist that assesses experience archives, recurring goals, exploration noise, and deferred goals. The checklist cross-references actual agent actions against expected outcomes to ensure newly learned features function without regressions.

Post-test verification for preventing regressions is a systematic evaluation that ensures an agent has correctly learned and implemented new behaviors. It examines logs and reasoning bank data to confirm operational integrity and validate that the updated logic functions as expected.

Yes, you can check deferred goals and exploration noise by examining the agent's reasoning bank data and journal entries. The verification process evaluates these specific sections against expected outcomes to confirm the agent is handling goal deferral and exploration correctly.

Agent learning validation requires pipeline records, journal entries, and reasoning bank data as evidence. These records provide the necessary operational logs to evaluate the agent's current state against the verification checklist and confirm expected outcomes.

Run learning verification immediately after an agent claims to have improved its goal-setting mechanism or implemented new features. This post-test check confirms the updated logic is functioning correctly and prevents operational regressions before deployment.