Graph-Grounded Agents

An AI agent can complete a difficult task and still leave the next run with almost nothing useful. Plans, evidence, failed attempts, and decisions remain trapped in conversation history. Add more workers, and the problem becomes expensive duplication, weak coordination, and results that are hard to verify.

Graph-Grounded Agents is a practical engineering playbook for turning temporary agent activity into connected, inspectable system state.

Inside, you will learn how to:

- build a bounded keep-or-reject improvement loop
- represent tasks, artifacts, trials, claims, evaluations, and approvals
- preserve experiment lineage without replaying full transcripts
- assemble compact context bundles from relevant graph neighborhoods
- divide multi-agent work without multiplying noise
- evaluate both final artifacts and the actions used to create them
- enforce provenance, permissions, budgets, and human gates
- progress from one measured script to a production-ready architecture

The book is framework-neutral. Its schemas, checklists, exercises, and 30-day plan can be implemented with files and Git, a relational database, a graph database, or a combination.

For engineers and technical leads moving from impressive demos to reliable agent systems, this book offers a disciplined path: make state visible, keep evidence connected, grant autonomy precisely, and retain meaningful human control.