ALTK‑Evolve: On‑the‑Job Learning for AI Agents

AI agents often fail to learn from experience, acting like an "eternal intern" who re-reads history without generalizing lessons. ALTK-Evolve is a memory system that addresses this by turning raw agent trajectories into reusable guidelines. The system captures agent interactions, extracts patterns to create rules, and then injects relevant guidance back into the agent's context at the moment of action. In benchmarks, this approach significantly improved agent reliability and success rates, showing the largest performance gains on difficult, multi-step tasks.