Multi-Agent Communication

Multi-agent communication encompasses the protocols, mechanisms, and patterns through which multiple AI agents interact, exchange information, and coordinate tasks. In complex AI systems, specialized agents frequently collaborate: an orchestrator coordinates sub-agents for research, writing, quality checking, and publishing. Dominant communication models: direct orchestration (a parent agent invokes sub-agents and integrates outputs), MCP (Model Context Protocol) from Anthropic as a standardized tool-call protocol between agents and external services, A2A (Agent-to-Agent Protocol) from Google as an open standard for peer-to-peer agent communication, and message queue-based systems for asynchronous communication. Critical design decisions: synchronous vs. asynchronous (synchronous is simpler, asynchronous scales better); push vs. pull; error handling (what happens when a sub-agent fails or times out?); state management (how is shared context kept consistent across agent boundaries?). Every agent-to-agent interface must be explicitly specified, versioned, and tested independently. Real-world example: a content creation multi-agent system consists of a Research Agent (fetches current data via MCP), Writing Agent (receives research output, generates draft), Quality Agent (checks draft against editorial rules), and Publishing Agent. Without clear communication contracts, multi-agent systems become brittle and difficult to debug.