Answer-preserving scaling exposes what smaller tests hide. If accuracy falls as irrelevant data grows, the retrieval architecture has become less effective. If an agent repeatedly fetches broad sets of records and discards the context, token costs will climb with the company.

Enterprises may hold trillions of tokens across customer, product, employee and service history. Even a million-token context window is a workspace, not organizational memory. The problem is not fitting everything into the prompt. It is identifying the smallest trustworthy set of facts needed for the decision at hand.

The harness can matter more than the model

Most enterprise AI starts federated. A model connects to a CRM, a support platform, an issue tracker and a document repository through APIs, and when someone asks a question, it retrieves from each source and assembles an answer. That works for discovery. It struggles the moment a question spans the operating model of the business, because the system has to reconstruct identity, relationships, permissions and current state on every single request.