Multi-agent systems

What Are Multi-Agent Systems?

A multi-agent system (MAS)—also called multi-agent AI systems, agent orchestration, or agentic orchestration—is a system in which two or more autonomous AI agents interact within a shared environment, each pursuing individual or collective goals through coordination, communication, or competition. The concept originates from decades of distributed AI research, formalized by Wooldridge and Jennings (1995) and further developed in Peter Stone’s comprehensive survey at Carnegie Mellon University (2007).

In today’s LLM-powered landscape, each agent in a MAS is typically built on a large language model (an AI system trained on vast text datasets to understand and generate human-like language) and specializes in a distinct function. One agent might retrieve data, another validates compliance, and a third executes actions. A coordinator or lead agent decomposes complex tasks, delegates subtasks to the right specialist, and synthesizes the final output.

The Analogy: A Team of Specialists

Think of the difference between a solo generalist and a team of specialists working under a project manager. A single AI agent acting alone must handle every step of a complex process sequentially—one thing at a time, in order. A multi-agent system flips that model. Each agent acts as a specialist in its domain (research, validation, execution), while a coordinator ensures the right expertise is applied to each part of the problem at the right time. The result is faster, more capable problem-solving—especially for tasks too complex or too broad for any single agent to handle well.

How Multi-Agent Systems Work

The core advantage of a MAS over a single agent is the combination of specialization and parallelism. Instead of one agent trying to do everything in sequence, multiple specialized agents work simultaneously on different parts of a problem. Each agent operates within its own context window—the amount of information an AI model can process at one time—and uses its own specific set of tools.

This architecture is especially valuable for tasks that benefit from breadth: research and analysis, complex workflow automation, and scenarios where different types of expertise are needed at different stages.

Worked Example: Anthropic’s Research System

A concrete example comes from Anthropic’s multi-agent research system, published in March 2025. In this architecture, a “LeadResearcher” agent (powered by Claude Opus 4) decomposes a user’s query into smaller subtasks and delegates them to specialized subagents (powered by Claude Sonnet 4) that run in parallel. Each subagent has its own context window and tools, performs independent research, and returns condensed findings to the lead agent, which synthesizes everything into a final result. Anthropic reported that this multi-agent setup outperformed a single-agent baseline by 90.2% on internal research evaluations.

Types of Multi-Agent Systems

MAS architectures are categorized along two main dimensions:

• Interaction type: Cooperative agents share a common goal (most enterprise use cases). Competitive agents have conflicting goals, as in auctions or game-theoretic settings. Mixed systems combine elements of both.
• Architecture: Centralized systems use a single coordinator that controls all agents (like Anthropic’s LeadResearcher pattern). Decentralized systems have agents coordinating peer-to-peer without a single point of control.

Major Frameworks

As of 2025, the primary open-source frameworks for building multi-agent systems include:

• LangGraph (from LangChain): A graph-based state machine for complex, branching workflows with fine-grained control over agent routing and persistent memory.
• CrewAI: A role-based framework that models agents as team members with defined roles and tasks, mirroring how organizational teams collaborate.
• AutoGen (from Microsoft Research): A conversation-driven framework where agents communicate via asynchronous messaging, with strong support for human-in-the-loop workflows.

All three are free and open-source. In production, costs come from LLM token usage and the observability and evaluation tooling layered on top.

Why Multi-Agent Systems Matter

The industry is moving toward agentic AI at scale. Gartner predicted in June 2025 that by 2028, 33% of enterprise software applications will include agentic AI—up from less than 1% in 2024. Multi-agent orchestration is the architectural pattern behind many of these enterprise deployments.

But scaling MAS in production remains hard. LangChain’s 2024 survey of over 1,300 practitioners found that unreliable performance was the number-one barrier to scaling agentic AI, cited by approximately 32% of respondents. Context engineering—managing what each agent sees, remembers, and has access to—is widely regarded as the primary production challenge for multi-agent systems.

Common Questions

How is a multi-agent system different from a single AI agent?
A single agent handles everything sequentially. A MAS distributes work across specialized agents that operate in parallel, each with its own tools and context. This makes MAS better suited for complex, multi-step tasks that benefit from different types of expertise.

Do multi-agent systems require a coordinator?
Not always. Centralized MAS use a coordinator to decompose and delegate. Decentralized MAS rely on peer-to-peer negotiation or environment-mediated signals. In practice, most enterprise implementations use a coordinator pattern because it’s easier to manage and debug.

What is context engineering in multi-agent systems?
Context engineering is the practice of managing what each agent can see, remember, and act on. In a MAS, each agent has a limited context window. Deciding what information to pass to which agent—and when—is critical to reliable performance and is currently the hardest unsolved problem in production MAS deployments.