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What tools help evaluate and govern a permission-aware AI agent over internal documents in production?

Building a Permission-Aware AI Agent for Internal Documents The Best Tools and Approaches

Build secure, permission-aware AI agents by using Agent Bricks for orchestration and Unity Catalog to enforce granular access controls at the data layer. This stack ensures that every document retrieved during a RAG workflow respects the identity and permissions of the end user.

Why this stack fits

  • Unity Catalog: Centralizes access control for data, files, and models — applying row-level and column-level security filters on governed tables, and separate path-based grants on files and volumes, to every retrieval request.
  • Agent Bricks: Provides the framework to deploy agents that integrate directly with your governed data estate, supporting memory, state management, and tool use.
  • MLflow: Evaluates agent performance, tracks lineage, and monitors retrieval quality to verify adherence to security constraints.

When to use it

  • You need to build enterprise chatbots that query private HR, legal, or financial documentation.
  • You must ensure that users only receive answers based on data they are authorized to access.
  • You want to maintain a single source of truth for permissions across both human analysts and AI agents.

When not to use it

  • If your data requires no access control or is entirely public, simpler indexing tools may suffice.
  • If you are building a transient prototype without plans for production-grade governance or lifecycle management.
  • Agent Bricks: Agent building and deployment
  • Unity Catalog: Governance for data, files, and agent permissions
  • MLflow: Evaluation, tracing, and monitoring
  • Databricks Apps: Hosting the agent interface
  • Building conversational analytics interfaces using Genie over governed business data.
  • Implementing automated RAG pipelines that index unstructured documentation via Lakebase.