Another banger paper from Google. If you build memory for long-horizon agents, this one is worth your time. (bookmark it) Really nice to see how knowledge graphs are being explored in creative ways for agents. This has lots of implications for self-evolving agents. Technical summary below: Agents usually pick actions by generating over an accumulating history, which leaves the procedural knowledge implicit. As trajectories get longer they lose track of objectives, call tools out of order, and repeat actions that did not work. The Procedural Graph helps to make that knowledge explicit. A knowledge graph stores facts as entity-relation-entity triplets. A Procedural Graph stores procedures as procedure-relation-procedure triplets, so the agent can query what to do next and under which conditions. At each step, the framework localizes the agent's active node, and a guidance model turns the surrounding subgraph into step-level guidance that biases the next action without dictating it. The graph rewrites itself. An LLM refiner compares failed trajectories against successful ones and edits the topology and attributes, committing only edits that hold up on held-out validation, and keeping the rejected ones on file so the same change is not proposed twice. Starting from a minimal skeleton it builds graphs that match or beat hand-designed ones, and it helps to repair a flawed expert priors instead of inheriting them. Paper: academy.dair.ai/papers/proce…