How AI agents can move beyond correlation to understand cause and effect, enabling more robust planning, better tool use, and reliable interventions in the real world
Learn how inverse reinforcement learning lets AI agents discover hidden reward functions by observing expert behavior, and why it matters for agent alignment and autonomous systems
Reimagining source code management from the ground up for AI agents, with intent based commits, simulation before merge, agent reputation, and automatic rollback contracts
Learn how AI agents can acquire complex behaviors by observing and mimicking expert demonstrations, from classical behavioral cloning to modern LLM agent distillation
Learn how knowledge distillation enables large, expensive AI agents to teach smaller, faster ones — reducing cost and latency while preserving capability
