Loop Engineering

Loop Engineering and Graph Engineering are rapidly emerging as important directions for the next generation of AI agents. Our work sits at the intersection of frontier research and real-world implementation. Rather than discussing these ideas only at a conceptual level, we build and test self-evolving agents in practice. We use real systems, long-horizon tasks, and failure cases to evaluate emerging research, understand what works, and identify what is still missing. In this session, we will share practical lessons from building self-evolving agent systems, including how agent workflows evolve from simple loops into graphs, how these graphs can adapt and rewrite themselves, and how agents can improve not only their outputs, but also the way they learn and evolve. The audience will gain both a clear understanding of the latest research and actionable insights from real-world experimentation.