Context Graphs for Explainable, Decision-Aware AI Agents
summary
Claims a concrete five‑stage decision workflow (frame causality and environment, pull global rules and precedent, run risk‑value analysis, act or escalate, write the full reasoning chain back into the graph) and gives a working prototype built on Neo4j with a memory graph and text‑to‑cypher tooling. Argues for reference class validation with the 99% vs 1% medical example, shows agents that record precedent into the graph so future agents inherit decisions, and demos escalation to higher‑authority agents; prototype records and reuses reasoning successfully.
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chapters (5)
- 0:00Introduction to context graphs
- 3:02Why context graphs for AI agents
- 6:54Decision-making framework
- 14:52Self-learning and accountability
- 15:55Call to action
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