Continual Learning for AI Agents: From Failures to Durable Improvements - Soheil Feizi, RELAI

Verifiable Continual Learning (VCL) enables AI agents to improve from experience without forgetting previous successes or introducing regressions. This approach shifts focus from simple model fine-tuning to a multi-layered strategy involving harness and memory updates. Because raw production logs lack the structure for testing, they must be transformed into replayable learning environments that simulate scenarios and define success metrics. Practical VCL implementation rests on four core principles: replayability of failures, holistic routing of fixes to the appropriate agent layer, lifelong regression-aware optimization, and operational efficiency. By treating regression as an internal mechanism rather than a post-hoc check, agents can continuously evolve while maintaining performance across past tasks. This framework provides a scalable, verifiable path for developers to refine agent behavior using logs, feedback, and instructions.