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Enterprise AI MCP Integration
Exploring how conversational AI assistants can work with existing business systems through Model Context Protocol, securely, with the user's own permissions respected end to end.
Last updated Oct 17, 2026
Problem
Problem being solved
AI assistants are only as useful as the systems they can reach. Business systems sit behind authentication and authorization rules, so connecting an assistant to them means exposing capabilities deliberately and keeping every request secure and accountable.
Architecture
- 01AI Assistantschat clients · agents
- 02MCP Serverstools · context
- 03API Gatewayssecurity · policy · routing
- 04Enterprise Servicesbusiness systems · data
Overview
This is an ongoing engineering project, described here at a general level. The idea: an AI assistant talks to an MCP server, and the server, not the assistant, is responsible for safely reaching the systems behind it.
Areas of focus
- Deliberate tool design: clear names, input schemas, and descriptions that an AI model can reason about
- Security first: standards-based authentication, least privilege, and no credentials held by the assistant
- Respecting permissions: the assistant can only do what the person using it is already allowed to do
- Reliability: predictable behavior, safe retries, and clear failure messages
- Observability: being able to trace what was requested, what ran, and what happened
Lessons learned so far
- Design the tool surface first. A few well-described tools work better than one tool per API endpoint.
- Identity is the hard part. Most of the effort goes into making sure every request carries the right permissions.
- Errors are part of the interface. The model reads your error messages, so they need to be clear and actionable.