What Is a Multi-Agent System: A Plain-English Definition
A multi-agent system is a design pattern where multiple AI agents, each with a defined role and capability, collaborate to complete a task that would be impractical for a single agent. The agents communicate by passing messages, sharing a workspace, or calling each other through an orchestration layer. The case for splitting work across multiple agents is analogous to the case for teams over individuals in human organisations. Complex tasks benefit from specialisation: a research agent can be optimised for searching and synthesising information, while a writing agent can be optimised for drafting clear, structured documents. A single generalist agent doing both may do neither as well. Additionally, tasks that exceed a single agent's context window can be distributed across multiple agents, each handling a portion. Multi-agent systems have two common architectural patterns. In hierarchical architectures, an orchestrator agent receives the high-level goal, breaks it down into subtasks, assigns them to specialist agents, receives their outputs, and assembles a final result. The specialist agents do not communicate with each other directly. In collaborative architectures, agents work in parallel and can communicate with each other, sharing intermediate results and updating their work based on feedback from peers. The terminology can be confusing. 'Agent' in this context means an LLM-powered entity with a defined role and a set of tools. 'Multi-agent system' means a coordinated system of such entities. Each agent in the system might itself run agentic workflows internally, reasoning over multiple steps before returning its output to the orchestrator.
How Multi-Agent Systems Work
A multi-agent system in production typically has three layers: individual agents, an orchestration layer, and a shared state or message-passing mechanism. Each individual agent is an LLM instance with a specific system prompt defining its role and capabilities, a set of tools it can use, and access to relevant context for its subtask. A research agent might have a web search tool, a document reader, and a summarisation capability. A review agent might have a fact-checking tool and access to a style guide. A coordination agent might have the ability to route messages between other agents and update a shared workspace. The orchestration layer manages the overall workflow: which agent runs when, what input it receives, where its output goes, and how errors are handled. Frameworks like LangGraph, CrewAI, AutoGen, and Autogen Studio provide scaffolding for building this orchestration. Teams building sophisticated multi-agent systems often develop custom orchestrators for tighter control. Consider a concrete example from a UK legal technology startup. They build a multi-agent system for due diligence report generation. The orchestrator agent receives a company name and report scope. It dispatches a research agent to gather publicly available information about the company. A financial analysis agent processes the financial data extracted by the research agent. A legal risk agent reviews contract documents provided as inputs. A synthesis agent receives all three agents' outputs and drafts the due diligence report. The orchestrator assembles the draft and passes it to a quality review agent that checks for consistency, gaps, and formatting. The final output is a structured report that a human solicitor reviews and signs off on. Building this as a multi-agent system rather than a single large prompt offers several advantages. Each agent's task is narrow enough to be handled reliably within a reasonable context window. Specialised system prompts for each agent produce better results than a single generalist prompt. If one agent's output is poor quality, it can be re-run without redoing the entire workflow. Parallelising the research, financial, and legal agents where their work is independent reduces total elapsed time.
Why Multi-Agent Systems Matter for AI Product Development
Multi-agent systems matter for product development when the task being automated is genuinely complex, long-running, or multifaceted enough that a single agent cannot reliably complete it. They represent the state of the art in AI automation for sophisticated knowledge work. For product teams, the business case for multi-agent architecture rests on complexity, quality, and parallelism. Complexity: some tasks genuinely require different types of reasoning or expertise that a single system prompt cannot capture well. Quality: specialist agents with narrowly defined roles and tailored prompts consistently outperform generalist agents on complex tasks. Parallelism: independent subtasks can run simultaneously in a multi-agent system, significantly reducing total completion time. The engineering costs are also real. Multi-agent systems are harder to build, debug, and monitor than single-agent systems. When a single-agent workflow fails, the failure is usually visible at the one point where something went wrong. When a multi-agent system fails, the failure might be in any agent, in the communication between agents, in the orchestration logic, or in the state management. Comprehensive logging of every agent action and message is not optional. It is the only way to diagnose failures in production. For UK products in regulated sectors, multi-agent systems raise important questions about accountability and audit trails. If an AI system that influences a consequential decision involves five agents collaborating, the audit trail must capture every agent's reasoning and every intermediate output, not just the final result. Under the EU AI Act, high-risk AI systems must maintain logs sufficient to understand how outputs were produced. Multi-agent systems must be designed to produce these logs from the start.
Common Use Cases in Production AI Products
Complex research and report generation is one of the clearest use cases for multi-agent systems. Tasks like due diligence reports, competitive intelligence summaries, regulatory impact assessments, and market research reports all involve gathering information from multiple sources, analysing it from different perspectives, and synthesising it into a coherent document. Multi-agent architectures decompose this naturally along the gather, analyse, and synthesise stages. Software development automation uses multi-agent systems to handle the full cycle of code generation: one agent for architecture design, one for implementation, one for test generation, one for code review, and one for documentation. Tools like Devin and Claude Code use multi-agent approaches to handle extended software development tasks. Customer service and support automation at scale uses multi-agent systems to handle escalating tiers of query complexity. A triage agent classifies incoming queries. Simple queries go to a resolution agent that handles them with standard responses. Complex queries go to specialist agents for billing, technical issues, or account management. Unresolved queries escalate to human agents with full context assembled by the agent system. Content production pipelines for media, e-commerce, and marketing use multi-agent systems to move from brief to published content. A research agent gathers supporting information. A drafting agent produces initial content. An editing agent applies style guidelines and compliance checks. An approval agent presents the final draft for human sign-off. Data analysis pipelines for business intelligence use multiple specialised agents for data retrieval, statistical analysis, visualisation generation, and natural language interpretation, producing analyst-quality reports from raw data without requiring an analyst to write code.
Related Concepts
AI agents are the building blocks of multi-agent systems. Each agent in a multi-agent system is itself an LLM-powered entity that can reason and act. Understanding how individual agents work, how they use tools, how they maintain state, and how their reliability characteristics affect overall system reliability is foundational knowledge for building multi-agent systems. Agentic workflows describe the behaviour of individual agents within a multi-agent system. Each agent runs an internal agentic workflow: reasoning, acting, observing results, and iterating. Multi-agent systems compose multiple agentic workflows with coordination logic between them. AI orchestration frameworks like LangGraph, CrewAI, AutoGen, and custom solutions provide the scaffolding for building multi-agent systems. These frameworks handle message passing, state management, agent invocation, and error handling. LangGraph is particularly well-suited to stateful multi-agent systems because of its explicit state machine model. CrewAI provides higher-level abstractions for collaborative agent patterns with less boilerplate. LangChain is the parent ecosystem for LangGraph and provides many of the tool integrations, memory components, and LLM wrappers that individual agents in a multi-agent system use. Understanding LangChain's component model helps when building multi-agent systems on LangGraph. Tool use is what gives each agent in a multi-agent system its capability. Each agent has access to specific tools appropriate to its role. A research agent might have web search and document reading. A calculation agent might have a code execution environment. A communication agent might have email and calendar APIs. The design of each agent's tool set is a key architectural decision that determines what the agent can reliably accomplish. AI guardrails at the multi-agent level need to cover not just individual agent outputs but also the communication between agents. An agent in a multi-agent system can be prompted to take harmful actions through messages from other agents in the system, a form of indirect prompt injection. Guardrails that validate inter-agent messages are part of building safe multi-agent systems.