Modern enterprise data management is shifting from simply storing static facts to preserving the logic behind autonomous decisions through Context Graphs and Agent Traces. Context Graphs function as a dynamic organizational memory by recording not just what happened, but the rationale, timing, and situational variables surrounding every action. Complementary to this, Agent Traces act as a detailed "call stack" for AI, documenting the probabilistic reasoning and specific data points used during multi-step workflows.

This technological evolution aims to eliminate the "Context Void" by employing bitemporal modeling to ensure historical decisions are understood within their original circumstances.

While these tools offer significant benefits for audits, fraud detection, and healthcare journey mapping, their implementation faces challenges regarding social complexity and the ethics of workplace surveillance.

Ultimately, this architectural shift transforms businesses into living, reasoning systems that leverage historical decision-making as a durable asset for future intelligence.