MCP Design Strategies for Enterprise AI Agents

To operate reliable AI agents in enterprise environments, process-centric MCP (Model Context Protocol) design, rather than API-centric design, is key.

Core Architecture Strategies:

• Process-Centric Custom MCP: Generic MCP servers that simply wrap APIs force the model to directly handle complex logic such as pagination, retries, and error handling, leading to hallucinations or task failures. Therefore, agents should be designed so that they only decide 'what to do,' while the actual complex and repetitive work is performed by deterministic tools.
• Strict Privilege Boundaries: When using tools with powerful permissions, such as the Microsoft Graph API, security must be enforced through tooling and configuration, not prompts. For example, physical restrictions are needed, such as granting specific permissions (like MFA resets) while excluding administrator accounts.
• Stabilizing the System Through Role Division:
  • OpenClaw: Handles communication channels such as Teams, webhooks, and routing, as well as session management, isolating credentials so they are not exposed to the model context.
  • Cursor CLI (via ACPX): Utilized as the runtime for the actual agent loop, as it excels at performing long-running tasks, creating sub-agents, and tracking work.