How to give AI agents access to your documents
To let an AI agent work with your documents, it needs addressable, permissioned access to where they live, not a one-time dump into the prompt. The cleanest way is a shared data layer the agent reaches over MCP, so it can read and write your documents under your control. This guide covers what that access really requires, how it compares to RAG, and how to keep confidential data safe.
What does "access to your documents" really require?
Pasting a few files into a chat is not access, it is a snapshot that goes stale and does not scale. Real access means the agent can find, read, and write the current documents on demand, with permissions that decide what it may touch. That requires a place the documents live and a protocol the agent uses to reach them.
MCP or RAG: which gives agents access?
They answer different needs. RAG retrieves passages from a static corpus into the prompt, which is good for question answering. MCP (Model Context Protocol) is a standard way for an agent to call tools and reach live resources, so it can read and write your actual documents, not just fetch text about them.
| RAG | MCP | |
|---|---|---|
| What it gives | Retrieved passages | Live read and write access |
| Data state | Snapshot at index time | Current documents |
| Actions | Read-only | Read, write, act |
| Best for | Q&A over static text | Agents working on your data |
How do you keep confidential documents safe?
Access and safety are the same design problem. The agent should reach documents through a layer with per-resource permissions, so it acts only where you allow, and credentials never sit raw in the prompt. Scoped, permissioned access is what makes it safe to point an agent at real work instead of toy examples.
How does adlass give agents access?
adlass is a shared data layer your agents reach over MCP. Your documents live in Spaces, the agent reads and writes them by reference, and per-Space permissions control exactly what it can do. You give the agent access to real, current documents without handing over your whole drive or pasting files into a chat.
Deeper guides
- How do I give an AI agent access to my company documents?
- MCP vs RAG for a knowledge base: which should you use?
- How do you use AI agents on confidential documents safely?
In this guide
- How do I give an AI agent access to my company documents?
Connect the agent to your documents through an MCP server, not a file dump. It reads and writes the current documents under per-resource permissions.
- MCP vs RAG for a knowledge base: which should you use?
RAG retrieves passages for question answering. MCP lets an agent reach and act on the live knowledge base. Use RAG for static Q&A, MCP for agents that do work.
- How do you use AI agents on confidential documents safely?
Reach documents through a layer with per-resource permissions, keep credentials out of the prompt, and scope access. That makes it safe to point agents at real work.
Frequently asked questions
- Can I just upload my documents to the chat?
- That works for a one-off, but it is a stale snapshot, it does not scale past a few files, and it gives no permissions. Real access means the agent reaches current documents on demand through a permissioned layer.
- Do I have to give the agent my whole drive?
- No. With per-Space permissions you scope access to exactly the documents the agent should touch, so it never sees more than you allow.
Work with your agents on the same data
adlass is the shared data layer where you, your team, and their agents work over the same documents and datasets.