Continual Learning for AI Agents: From Failures to Durable Improvements - Soheil Feizi, RELAI
Jul 5, 2026 · 22:35
Soheil Feizi, founder of RELAI, presents verifiable continual learning for AI agents, a framework that turns production failures into replayable tests and ensures fixes improve without breaking past capabilities. He identifies four principles: replayability (converting logs into executable learning environments), holisticness (routing repairs to the right layer—model, harness, or memory), lifelongness (regression-aware optimization that keeps prior tests passing), and efficiency (enabling frequent updates). Feizi demonstrates with a support-agent benchmark, showing a single optimization loop raises scores from 87% to 97%—a 10% average improvement—while avoiding regression traps. The system, available at relai.ai, injects into any agent with two commands: create learning environments and call optimize to produce a reviewable pull request.
