Picture a claims agent that demos flawlessly — then fails in week one of a pilot. Not because the model is weak; it's excellent. It fails because it answers a coverage question from a policy document that was superseded two renewals ago. The agent doesn't know. Nothing told it what was current, what was authoritative, or what this particular adjuster was allowed to see. (A composite of a pattern, not a single customer.)
This is the quiet reason so many agent programs stall, and it has a number attached. Less than 1% of enterprise data resides in today's large language models ‹IBM, AI Value Creators›. The other 99% — the policies, the claim notes, the servicing history, the documents that actually decide a case — lives in systems the model has never seen. When an agent answers without that context, it isn't reasoning. It's guessing, fluently.
The instinct is to fix this agent by agent: wire this one to the policy system, that one to the CRM, bolt on a retrieval step, ship it. It works in the demo.
Then the second agent needs the same data with different permissions, the document changes, an auditor asks why a decision was made, and the whole arrangement reveals itself for what it is — point-to-point plumbing with no shared notion of what's true. BCG, studying insurers specifically, puts the root cause plainly: efforts are “fragmented across siloed pilots” on “outdated” data foundations, and the way out is to “use AI to unify data and workflows” ‹BCG, AI-First Companies›.
That's what a context layer is for. Not another database, and not enterprise search — a governed layer between your systems and your agents whose entire job is to answer one question correctly, every time an agent asks it: given who is asking and what's true right now, here is the context you're cleared to use, and here is where it came from. Get that right and grounding, explainability, and audit stop being features you add later. They become things the architecture already does.
[Continued… — full article to be written. Author byline [PLACEHOLDER].]