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

Agents fail in production in ways that static benchmarks cannot fully capture. The key question is whether they can learn from those experiences without drifting or breaking prior capabilities.

This talk introduces verifiable continual learning for AI agents: a framework for converting traces, failures, and feedback into testable, regression-aware improvements. I will discuss four core requirements: turning failures into replayable learning environments, preserving prior capabilities during updates, routing repairs to the right layer of the agent stack, and keeping the learning loop efficient enough to run continuously.

We will use these principles to examine today’s approaches, including prompt optimizers, memory consolidation, coding-agent harness repair, and trace-to-harness systems. I will then discuss the remaining gap: a holistic, lifelong, verifiable learning loop with online regression control.

Speakers:
- Soheil Feizi (RELAI): Dr. Soheil Feizi is the Founder and CSO of RELAI and an Associate Professor of Computer Science at the University of Maryland, College Park, whose work focuses on the reliability, safety, and optimization of AI systems.
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