Context Engineering

AS SYSTEMS GO LIVE, A QUIETER REALIZATION IS SPREADING: LASTING ADVANTAGE WILL COME LESS FROM THE MODEL AND FAR MORE FROM THE CONTEXT WRAPPED AROUND IT.

- BY ROHAN NARAYANA MURTY, FOUNDER, WORKFABRIC AI

Over the past year, the enterprise AI conversation has revolved around which model to use, which cloud to choose and how quickly pilots can be shipped into production. As systems go live, a quieter realization is spreading: lasting advantage will come less from the model and far more from the context wrapped around it.

Context, the data, rules, workflows and tacit tribal knowledge that make each organization unique, is becoming the real raw material of enterprise AI and the place where advantage lives. That shift opens up a large, underappreciated entrepreneurial opportunity. Think of context engineering as the craft of making models behave as if they were hired into a specific business. Instead of maintaining long lists of rules in code, teams curate everything that expresses how the organization really works, from policy manuals and workflow logs to tickets and chat histories. They then design the pipelines and guardrails that let models use that context safely and reliably.