MCP vs A2A vs ACP Explained : The Future of AI Agents

MCP, A2A, and ACP are three acronyms you keep hearing in the AI agent world—but what do they actually mean? In this video, we break down *Model Context Protocol (MCP), Agent2Agent (A2A), and Agent Communication Protocol (ACP)* in simple terms. You'll learn: • What MCP actually does • How MCP connects AI agents to tools, data, and external systems • What A2A means and how AI agents communicate with other agents • Why MCP and A2A are complementary rather than direct competitors • What happened to ACP and why its work is now part of the A2A ecosystem • How an orchestrator can coordinate multiple specialized AI agents • Why agent interoperability could become critical infrastructure for the next generation of AI • The security and reliability challenges of multi-agent systems The simple mental model: *MCP = Agent → Tools* *A2A = Agent → Agent* *ACP = Historical agent-communication work now incorporated into A2A* The bigger idea? We're moving from AI systems that work in isolation toward networks of specialized agents that can use tools, delegate tasks, and collaborate. If you're building or following AI agents, MCP and A2A are protocols worth understanding.