Stephanie Baladi

AI agents need more than just enterprise search to do reliable work. They need an enterprise context layer that retrieves the most authoritative information from connected systems, understands how that information relates across tools and teams, enforces source permissions every time, and delivers the right evidence at each step of a workflow.

That context layer is the piece most enterprise AI evaluations still overlook. Teams compare model quality, context window sizes, and connector counts. All of these are important, but once AI moves from answering one-off questions to helping with research, decisions, drafting, and execution, the harder problem is context quality — whether the system can ground every step of every workflow in current, relevant, permissions-aware enterprise knowledge.

The difference is measurable: in Glean’s evaluation write-up, Not all enterprise context is created equal , human graders who expressed a preference chose answers grounded in Glean’s context layer as correct 1.9× as often as those built on ChatGPT’s company knowledge for complex enterprise queries. Weak context makes agents work harder and trust less; strong context lets them retrieve precisely, pass less noise, and produce answers people can rely on.