Comparison of Agentic AI and Multi-Agent System Architectures

Technical analysis distinguishes between agentic AI and multi-agent architectures. An agentic system is characterized by its ability to autonomously identify goals, select tools, and reflect on results to meet completion conditions based on a system prompt. While multi-agent systems involve multiple specialized agents coordinating and collaborating to achieve tasks, they introduce significantly higher complexity in management and construction.

To facilitate communication within these systems, various open protocols are utilized. The Model Context Protocol (MCP) focuses on agent-to-tool interactions, while the Agent2Agent (A2A) protocol addresses agent-to-agent communication. Other alternatives include ACP, ANP, and AG-UI. Selecting the correct protocol is critical to avoid custom integration overhead and vendor lock-in, as the number of required integrations scales with the number of agents and tools used.