Working with agents over shared data

Guides

• Token efficiency is a context problem

  AI agents waste tokens because they reload the same context on every step. The fix is a shared data layer they read from by reference, not a shorter prompt.

  • Why does my agent re-read the same file and waste tokens?
  • Why does my agent lose context between sessions?
• Shared context for agents: how teams and agents work on the same data

  When every agent and person holds its own copy of the context, work gets overwritten and drifts out of sync. A shared data layer gives everyone one source of truth.

  • How do multiple agents work on the same files without conflicts?
  • How do you hand off context between agents?
  • How can a team and their agents work on the same documents?
• How to give AI agents access to your documents

  Agents need addressable, permissioned access to your documents, not a one-time dump into the prompt. This guide covers MCP, RAG, and keeping confidential data safe.

  • 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?

Comparing approaches?

See how adlass compares to RAG and other ways of giving agents access to data.