A multi-agent system, in the context of modern AI, is an architecture in which several distinct AI agent instances interact with one another, typically by cooperating, dividing labor, or checking each other's work, to accomplish a task that a single agent would handle less effectively alone. Each agent is usually built on a Large language model, often given a distinct role, persona, or set of tools through its own System prompt, and the agents communicate by passing messages, sharing a workspace, or reading and writing to a common piece of state.
The concept of multi-agent systems is much older than large language models, with a long history in distributed artificial intelligence and Reinforcement learning research studying how independent agents cooperate or compete within a shared environment. The LLM-specific version of the idea grew directly out of the broader agentic wave of 2023 to 2025: as individual agents became reliable enough to complete multi-step tasks using Tool use (function calling), researchers and developers began composing multiple agents together to handle tasks with natural role divisions, such as software development, research, or debate.
Common patterns
Several organizational patterns recur across multi-agent frameworks. In an orchestrator-worker pattern, one agent breaks a task into subtasks and dispatches them to specialized worker agents, then combines their results, a structure well suited to tasks that decompose naturally, such as researching several subtopics in parallel. In a debate or critique pattern, two or more agents propose answers or critique one another's reasoning before a final answer is produced, intended to surface errors a single model might not catch on its own. In a pipeline pattern, agents are arranged in a fixed sequence, each handling one stage of a larger process, such as a drafting agent followed by an editing agent and a fact-checking agent. Some systems also use a shared blackboard or document that multiple agents read from and write to asynchronously, rather than passing messages directly to one another.
Advantages and rationale
Proponents argue that dividing a complex task among specialized agents, each with a narrower role and correspondingly simpler Prompt, can produce more reliable results than asking one agent to hold an entire complex task in mind at once, particularly for tasks that exceed a single Context window or that benefit from independent, less-correlated attempts before a result is chosen. Multi-agent debate and critique setups in particular have been studied as a way to reduce Hallucination (AI) and reasoning errors, on the premise that an agent reviewing another agent's output from a fresh context is more likely to catch a mistake than the original agent reviewing its own work.
Limitations and criticism
Multi-agent systems introduce their own costs and failure modes. Running several agents multiplies token usage and latency compared to a single agent, since each agent typically maintains its own context and may call the underlying model many times. Coordination failures are also common: agents can talk past each other, duplicate work, get stuck waiting on one another, or drift from the original task as errors compound across handoffs, a risk closely related to the reliability concerns discussed for single agents. Some practitioners have argued that many tasks marketed as requiring multiple agents can be handled just as well, and more cheaply, by a single sufficiently capable agent with good Prompt engineering and access to the right tools, making multi-agent architecture a genuine engineering trade-off rather than a strictly superior default. As of 2025, multi-agent frameworks remained an active area of tooling development, with no single dominant standard for how agents should communicate, though the Model Context Protocol and related efforts to standardize tool and context interfaces have been extended by some projects toward agent-to-agent communication as well.