Memory layer for AI agents
Map memory layer to Atlan's architecture
"Memory layer" is the market term used by agent frameworks. 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.
note
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 |