LLM agents perform better when guided by learned procedural graphs that self-improve through analyzing successes and failures, rather than relying on unconstrained action generation from conversation history.
This paper introduces Procedural Graphs, a structured framework that organizes step-by-step instructions for LLM agents into graph-based knowledge structures. Instead of letting agents freely generate actions from scratch, the system guides them through a learned graph of procedures, relationships, and conditions.