Google paper proposes Procedural Graphs to make long-horizon agents' procedural knowledge explicit
omarsar0 · x · 2026-09-10
A new Google paper introduces the Procedural Graph, an approach to making procedural knowledge explicit for long-horizon agents. Today, agents pick actions by generating over an accumulating history, so as trajectories grow they lose track of objectives, call tools out of order, and repeat failed actions.
Key ideas:
- Knowledge graphs store facts as entity-relation-entity triplets; Procedural Graphs store procedures as procedure-relation-procedure triplets, letting the agent query what to do next and under which conditions
- At each step the framework localizes the agent's active node, and a guidance model steers it
The author highlights implications for self-evolving agents, and recommends it to anyone building memory for long-horizon agents.
Related event: Google Proposes Procedural Graphs to Steer Long-Horizon AI Agents(3 posts)→
More from coding & agent
- AWS on agent prompt caching: explicit + implicit patterns to slash token costs — AWS ML Blog · 2026-10-27
- He gave his AI agent a phone number and email — then hired it as social media manager — heyshrutimishra · 2026-10-27
- Firecrawl launches Alexandria company-data API and announces $75M Series B — devdigest · 2026-10-27
- 'Let an agent run wild on vague goals and it makes your code worse 9 out of 10 times' — eptwts · 2026-10-27
- LangChain to demo the in-house design agent its team uses daily, this Wednesday — LangChain · 2026-10-27
- Biotech Agent Series Day 8: A Notebook Review Agent to Auto-Audit Lab Records — nlarusstone · 2026-10-27