A multiagent system is an AI architecture in which multiple AI agents — each with its own role, tools, or specialization — interact, coordinate, or negotiate to accomplish a goal that a single agent could not. Gartner names multiagent systems as the evolution of agentic AI in its Top Strategic Technology Trends for 2026.
How it works
Instead of one agent looping over a task, a multiagent system splits work across specialists — e.g., a planner agent, a research agent, a coding agent, and a reviewer agent — that pass context and results between each other. Coordination relies on shared protocols: Anthropic’s open Model Context Protocol (MCP), launched November 2024, became the de-facto standard for agent–tool interoperability, and frameworks like OpenAI’s Agents SDK, AutoGen, and CrewAI orchestrate the hand-offs.
Why it matters
Multiagent systems are the named successor to single-agent “agentic AI” in analyst language. Gartner expects 40% of enterprise applications to embed task-specific agents by end-2026 (from <5%), even as it warns 40%+ of agentic projects will be canceled by 2027. See Enterprise AI Statistics 2026 for the adoption data.
Related terms: AI Agent · Large Language Model · Generative AI · All glossary entries