How to build multi-agent systems with MCP

AI agents excel when assigned focused tasks, but their efficiency can degrade when faced with multifaceted tasks involving multiple tools and decisions. Multi-agent systems address this by dividing tasks among specialized agents, each dedicated to a specific part of the overall job. The Model Context Protocol (MCP) facilitates this process by providing a common language for AI agents to interact with tools and data, regardless of the platform. Zapier's MCP server exemplifies this by allowing seamless interaction with over 9,000 apps and 30,000 actions, all while ensuring secure operations through OAuth-managed connections. This system enables agents from platforms like Claude, ChatGPT, or Gemini to perform tasks such as sending Slack messages or updating Salesforce records without additional setup. By delineating roles and tools for each agent, and utilizing MCP servers, multi-agent systems can operate efficiently, scaling as needed while maintaining context integrity and security.