AI Glossary · Agentic AI
Multi-Agent System
An architecture where multiple specialized agents (planner, researcher, executor, critic) collaborate to complete complex tasks.
Definition
What is Multi-Agent System?
Multi-Agent System is an architecture where multiple specialized agents (planner, researcher, executor, critic) collaborate to complete complex tasks.
- Category
- Agentic AI
- Glossary set
- 11 related terms
- Audience
- Enterprise AI leaders
Why does Multi-Agent System matter in enterprise AI?
Multi-Agent System matters in agentic AI programs because it shapes how autonomous workflows plan, use tools, escalate exceptions, and produce auditable outcomes.
Related terms in Agentic AI
- Agentic AI
- AI systems that pursue goals autonomously by planning multi-step actions, calling tools and APIs, and revising their approach based on feedback — rather than only responding to a single prompt.
- AI Agent
- A software entity built around an LLM that perceives context, reasons over a task, invokes tools, and produces an outcome — often on behalf of a user or another agent.
- Autonomy Level
- A taxonomy (advisor → co-pilot → supervised agent → autonomous agent) describing how much human oversight a workflow requires.
- Digital Employee
- A packaged agent (with a role, tools and SLAs) that performs a bounded function — collections, scheduling, tier-1 support — measured like a human FTE.
- Function Calling / Tool Use
- A model capability that produces structured JSON to invoke external functions, APIs or databases — the mechanism behind most agentic workflows.
- MCP (Model Context Protocol)
- An open protocol for exposing tools, data and prompts to AI models in a standardized way, simplifying agent-to-system integration.