Context layer
A context layer is the connecting layer between a company's data and its AI agents, aggregating knowledge from fragmented sources.
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What is a context layer?
A context layer is the infrastructure between a company's raw data and its AI agents. It aggregates a company's knowledge into one layer, so agents across different tools and departments draw on the same context instead of each rebuilding their own picture of the company.
Company knowledge is fragmented and lives in Slack channels, an internal wiki, someone's memory, or an undocumented exception. Every source is its own island: Slack, Notion, the CRM, a shared drive. A context layer sits above all of them as one unifying layer, pulling from every source, aggregating what it finds, and connecting it, so what was scattered across a dozen places comes together in one place an agent can use.
Why do agents fail without context layer?
Enterprise agents and enterprise AI applications often fail because of context, not because of insufficient model quality. The model doesn't understand your processes, your business, your customers, or your intent, and real context is what solves that. An agent working without it can give an answer that sounds right and sounds true, but isn't.
A context layer is different from context engineering. Context engineering runs inside a session, while a context layer feeds your agents' context across every session and every agent you run. Engineering can only work with what the layer gives it: perfect engineering on top of a bad layer still produces confident wrong answers.
How does a context layer work?
A context layer pulls in data from every connected source and turns it into entities, the people, customers, and products a company actually deals with, and the relationships between them. It collapses duplicates into one record, captures the tribal knowledge nobody wrote down, applies permissions at the point of retrieval, and keeps all of it current as sources change.
A vector database indexes content by similarity. RAG fetches the nearest chunks. Neither resolves duplicate entities or checks permissions. A context repository stores knowledge once, keeps it current, versions it, and applies appropriate access controls to the file.