If your agent isn’t using procedural memory, it’ll attempt to solve the same problems from scratch in each new session. Procedural knowledge trapped in scattered markdown and text fragments won’t scale your projects to production, but graph engineering can help.

Join our webinar to see how representing a procedure as a graph surfaces structure that prose hides, including inputs, outputs, dependencies, and provenance. Learn how Agent Instruction Protocol (AIP) compiles skills into typed, schema-validated graphs, boosting Claude Sonnet's pass rate from 53% to 67% on SkillsBench. We'll discuss how to apply this to real enterprise workflows using the Neo4j Agent Memory Service (NAMS), distilling historical decision traces into a portable, provenance-grounded skill with human-in-the-loop review and continuous validation. 

You’ll learn:

• Why graph engineering needs a portable standard, not more orchestration 
• How graph representations provide a more scalable, governed way to store and retrieve procedural knowledge 
• How procedural memory improves agent task success, consistency, and long-term intelligence