Multi-Agent System
An architecture where multiple AI agents collaborate, each with specialized roles or capabilities, to accomplish complex tasks that no single agent could handle alone.
Why It Matters
Multi-agent systems enable tackling enterprise-scale problems by dividing work across specialized agents that can work in parallel and cross-check each other's work.
Example
A coding multi-agent system where one agent writes code, another writes tests, a third reviews for security, and a fourth manages the overall workflow.
Think of it like...
Like a hospital surgical team — the surgeon, anesthesiologist, nurse, and technician each have specialized roles but work together on the same patient.
Related Terms
AI Agent
An AI system that can autonomously plan, reason, and take actions to accomplish goals. Unlike simple chatbots, agents can use tools, make decisions, execute multi-step workflows, and adapt their approach based on results.
Planning
An AI agent's ability to break down complex goals into a sequence of steps and determine the best order of actions to accomplish a task. Planning involves reasoning about dependencies, priorities, and contingencies.
Agentic AI
AI systems designed to operate with high autonomy — planning, executing, and adapting without constant human oversight. Agentic AI emphasizes independent action-taking to accomplish user goals.
Orchestration
The coordination and management of multiple AI components, tools, and services to accomplish complex workflows. Orchestration handles routing, sequencing, error handling, and resource allocation.
Swarm Intelligence
Collective behavior emerging from the interaction of multiple simple agents that together produce sophisticated solutions. Inspired by natural swarms like ant colonies, bee hives, and bird flocks.