Memory layer for AI agents
Map memory layer to Atlan's architecture
"Memory layer" is the market term used by agent frameworks.
Atlan delivers the same outcome as the context layer for AI: a governed tier that gives agents persistent, trustworthy memory of your data through metadata and MCP.
Atlan doesn't ship a standalone product named "memory layer." The same outcome comes from two components working together:
- Context repository: a bounded, versioned package of governed context for a domain. It holds the semantic model, business logic, verified question-answer pairs, and the artifacts an agent uses to answer questions about that domain. Context repositories are built in Context Studio.
- Atlan MCP server: the interface that lets AI agents search, inspect, and update Atlan metadata from their runtime using governed, real-time context.
The practical distinction matters. Atlan is a fit for governed, shared enterprise context across agents and runtimes. It's not a drop-in replacement for a lightweight per-agent personalization store such as a standalone vector database.
Context Studio is in Private Preview. Availability depends on your tenant.
Memory layer vs vector database vs RAG
A memory layer isn't the same as RAG or a raw vector database.
- RAG retrieves passages from a corpus at query time.
- A vector database stores embeddings for similarity search.
- A memory layer persists and re-injects context across runs.
In Atlan's model, the governed context layer is the enterprise substrate that agents read from, with lineage, policy, ownership, and semantic meaning attached.
| Approach | Persistence | Governance | Best fit |
|---|---|---|---|
| Vector database | Per-agent or per-app | Application-level | Single-agent personalization |
| RAG pipeline | Query-time retrieval | Depends on corpus controls | Retrieval from a document corpus |
| Atlan context repository + MCP | Versioned, shared context across runs | Enterprise governance, lineage, policy, and RBAC | Multi-agent, multi-runtime enterprise deployments |