Context Graphs for Explainable, Decision-Aware AI Agents — Andreas Kollegger & Zaid Zaim, Neo4j
May 28, 2026 · 16:39
Andreas Kollegger and Zaid Zaim of Neo4j argue that context graphs transform AI agents from knowledge-driven to decision-aware by providing the "why" behind actions through policies, rules, and precedent. They present a five-stage decision framework: frame the problem with causality and environment, pull global rules and past precedent, run risk value analysis (including reference class validation—e.g., a drug correct 99% of the time may be fatal for the 1%), decide to act or escalate to authority, and write the full reasoning chain into the graph. This last step turns every decision into precedent for future agents. The talk emphasizes explicit risk assessment, such as whether a decision is reversible and what is being maximized, and uses examples like medical prescribing and financial loan eligibility to illustrate how agents must know their reference class before acting.
