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Glossary

Multi-Agent Systems

Multi-agent systems are architectures where multiple specialised AI agents collaborate, communicate, and coordinate to solve complex tasks that would be difficult or impossible for a single agent to handle alone.

What are Multi-Agent Systems?

A multi-agent system (MAS) is an architecture where multiple AI agents — each with specialised capabilities, knowledge, or roles — work together to accomplish complex goals. Rather than relying on a single monolithic agent to handle everything, multi-agent systems divide work among specialists that collaborate through structured communication. Think of it as a digital team. One agent might research a topic, another analyses the findings, a third writes a report, and a coordinator ensures the workflow proceeds smoothly. Each agent can be optimised for its specific role, using different models, tools, or configurations as needed.

How Multi-Agent Systems Work

Multi-agent systems typically follow one of several coordination patterns. In hierarchical systems, a lead agent delegates tasks to specialist agents and synthesises their outputs. In collaborative systems, agents work in parallel on different aspects of a problem and share their findings. In adversarial systems, agents critique or challenge each other's work to improve quality. Communication between agents can be structured (passing defined data formats) or conversational (agents discussing in natural language). The orchestration layer manages agent lifecycle, message routing, error handling, and resource allocation. Frameworks like AutoGen, CrewAI, and LangGraph provide infrastructure for building multi-agent systems, handling the complexities of agent communication, tool sharing, and workflow management.

Why Multi-Agent Systems Matter for Business

Multi-agent systems excel at tasks that are too complex for a single agent — tasks requiring multiple types of expertise, parallel processing, or iterative refinement. A research workflow might need agents specialised in data gathering, statistical analysis, and report writing. A software development workflow might use agents for coding, testing, and code review. The specialisation advantage is significant. A single agent trying to be an expert at everything often performs worse than multiple specialists focused on their domains. Multi-agent systems also improve reliability through redundancy — if one agent produces a questionable result, others can verify or correct it. However, multi-agent systems add complexity. Coordination overhead, error propagation between agents, and debugging distributed workflows are all challenges that need careful management.

Practical Applications

Multi-agent systems are being deployed for complex research and analysis workflows (where agents handle different aspects of investigation), content creation pipelines (where agents research, write, edit, and fact-check), software development teams (where agents code, review, and test), and business process automation (where agents handle different stages of a workflow). In enterprise settings, multi-agent systems often mirror existing organisational structures, with agents taking on roles similar to human team members. This makes them intuitive to design and manage for business stakeholders who understand team-based workflows.

FAQ

Frequently asked questions

Use multi-agent systems when a task requires multiple distinct expertise areas, benefits from parallel processing, or needs quality checks through agent collaboration. Use a single agent for straightforward, well-defined tasks. The added complexity of multi-agent systems should be justified by meaningful improvements in capability or quality.

Agents typically communicate through structured messages, shared memory, or natural language conversations. The orchestration layer manages message routing and ensures agents receive the context they need. Some systems use a shared workspace that agents read from and write to.

Multi-agent systems use more compute than single agents because multiple models run simultaneously. However, using smaller specialised models for each agent role can be more cost-effective than a single large model. The cost depends heavily on the architecture design and model choices.

Related Content

AI Agents

The building blocks of multi-agent systems.

Agentic AI

The broader paradigm of autonomous AI systems.

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