How is a context layer different from a knowledge base?

A knowledge base stores documents. A context layer stores the relationships between them, including how a decision, a conversation and a piece of work connect to each other.

How is a context layer different from RAG?

RAG is a retrieval technique used for a single request. A context layer is persistent infrastructure: it holds the company’s connected state across sessions and agents, and retrieval is one thing it serves.

Does a context layer replace my existing systems?

No. Systems of record stay authoritative. The context layer reads across them and holds the connections between them, which is the part no single system owns today.

Context Layer

A context layer is the part of an AI system that holds a company’s connected knowledge, so every agent and assistant reasons from the same understanding of the business.

Most AI tools start every conversation from zero. They can search a document or answer a question, but they do not retain how that document connects to a decision, a customer, or a piece of work already in progress. A context layer changes that. It sits between a company’s connected tools and its AI agents, holding the relationships between documents, conversations, people and decisions, not just the documents themselves. When an agent needs to act, it reasons from that structure instead of a single retrieved passage.

This is also what makes governed execution possible. An agent can only act responsibly on company context if it has a persistent, structured understanding of that context to act on, and a consistent view of who is allowed to see which part of it.

Context Layer

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