Episode Details

This story was originally published on HackerNoon at: https://hackernoon.com/context-graphs-building-production-world-models-for-the-age-of-ai-agents.
AI writes code, but lacks production context. Context graphs capture decision traces to build real world models.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #context-graphs-enterprise-ai, #decision-trace-architecture, #production-world-models-ai, #cross-system-infrastructure, #learned-enterprise-ontology, #agent-structural-trajectories, #organizational-ai-simulation, #good-company, and more.

This story was written by: @playerzero. Learn more about this writer by checking @playerzero's about page, and for more stories, please visit hackernoon.com.

AI struggles in production because enterprises store state, not decisions. Code, CRMs, and tickets show what happened—but not why. Context graphs solve the “two clocks problem” by capturing decision traces across time, ownership, semantics, and outcomes. As agent trajectories accumulate, they form production world models that enable simulation, auditability, and compounding organizational intelligence.