I keep seeing the same pattern with enterprise AI agents: they look fine in demos, then break once they’re embedded in real workflows.

This usually isn’t a model or tooling problem. The agents have access to the right systems, data, and policies.

What’s missing is decision context.

Most enterprise systems record outcomes, not reasoning. They store that a discount was approved or a ticket was escalated, but not why it happened. The context lives in Slack threads, meetings, or individual memory.

I was thinking about this again after reading Jaya Gupta’s article on context graphs, which describes the same gap. A context graph treats decisions as first-class data by recording the inputs considered, rules evaluated, exceptions applied, approvals taken, and the final outcome, and linking those traces to entities like accounts, tickets, policies, agents, and humans.

This gap is manageable when humans run workflows because people reconstruct context from experience. It becomes a hard limit once agents start acting inside workflows. Without access to prior decision reasoning, agents treat similar cases as unrelated and repeatedly re-solve the same edge cases.

What’s interesting is that this isn’t something existing systems of record are positioned to fix. CRMs, ERPs, and warehouses store state before or after decisions, not the decision process itself. Agent orchestration layers, by contrast, sit directly in the execution path and can capture decision traces as they happen.

At scale, agent reliability depends less on model intelligence and more on whether past decisions are actually remembered.