Multi-Agent Systems

Multi-agent systems are architectures where multiple AI agents collaborate, coordinate, and sometimes compete to accomplish tasks that exceed the capability of any single agent. They represent the next frontier beyond individual AI assistants.

In a multi-agent system, different agents may specialize in different roles—one researches, another writes code, a third reviews and tests—much like a human team. Frameworks like CrewAI, Microsoft's AutoGen, and LangGraph provide the orchestration layer for these systems, defining how agents communicate, delegate tasks, share context, and resolve conflicts.

The practical applications are already substantial. In agentic engineering, multi-agent workflows can decompose a complex software project into parallel workstreams: one agent designs the database schema, another builds the API, a third creates the frontend, and a coordinator ensures they integrate correctly. The Agent Almanac—a directory of AI agents, MCP Tool Use & Function Calling servers, and agentic tools—was itself discovered, analyzed, and indexed by an AI agent, demonstrating how agents can curate the expanding ecosystem of other agents.

The Model Context Protocol is particularly important for multi-agent systems because it provides a standardized way for agents to discover and use shared tools. Without a common interface, every agent-to-agent and agent-to-tool combination requires custom integration. With MCP, agents can dynamically discover what capabilities are available and coordinate around them.

Multi-agent systems also raise new questions about emergent behavior. When agents interact at scale—discovering other agents, negotiating resources, competing in marketplaces—the system-level behavior can differ significantly from what any individual agent was designed to do. This is both the promise (emergent intelligence) and the challenge (emergent unpredictability) of the approach.