A group of AI agents that work together, side by side or one after another. They take on problems that are too complex, too large or too spread out for one agent.
A multi-agent system (MAS) is an AI architecture in which several autonomous agents, each with its own specialism, knowledge and skills, work together on tasks too complex, large or spread out for one agent. The agents can run in parallel, hand work to each other and combine their results under a central orchestrator.
In a multi-agent system, a supervisor agent, also called an orchestrator, receives a broad goal. It breaks the goal into smaller tasks and sends each one to a specialised worker agent. Workers run at the same time or in order, depending on which tasks rely on others. When they finish, the results go back to the orchestrator, which combines them into one clear output.
Compared with a single agent, this approach has four main advantages:
A central orchestrator breaks a goal into smaller tasks and sends them to specialised worker agents.
Agents run one after another. Each agent's output is the next one's input. This suits structured, ordered workflows.
Several agents work on different parts of a task at the same time. Their results are combined at the end.
Agents are arranged in layers. Senior agents plan and supervise. Junior agents carry out specific actions.
Government work often spans several domains. One complex citizen enquiry may touch housing, benefits, immigration and healthcare databases at once. A multi-agent system fits this well. Each domain has its own specialised agent, the agents run in parallel, and an orchestrator combines their work into one response.
For example, imagine a citizen submits a social housing application. A document agent pulls the information out of the uploaded files. A verification agent checks eligibility criteria across three benefit systems. A priority agent works out the applicant's place on the waiting list. A communications agent drafts a reply explaining the outcome. The agents run in parallel, so the work could take seconds rather than days.
Agents send each other structured messages through an orchestration layer. A supervisor agent breaks a goal into smaller tasks, sends them to worker agents and collects the results. Worker agents can also call tools such as APIs and databases, consult knowledge sources, or start sub-agents of their own. Communication protocols set the message format. They also set how an agent reports that it has finished or failed, and how partial results are combined.
A single agent works through a task step by step. It is limited by its context window, meaning how much it can hold in view at once, and by what it knows. A multi-agent system spreads the work across specialised agents. Research, drafting and verification agents can work on different parts of a complex task at the same time, which can sharply cut the time it takes. A MAS can also improve accuracy. A specialised agent is better at its narrow task than a generalist agent trying to do everything.
Here are three illustrations. In a citizen services MAS, a triage agent sorts requests and domain agents handle housing, benefits or immigration queries. A verification agent checks eligibility and a communication agent sends the response. In a regulatory compliance MAS, agents watch different areas of regulation at the same time. In a smart city operations MAS, agents monitor traffic, utilities, safety and environmental sensors in parallel and coordinate responses.
Microservices are software components that follow fixed logic, so their behaviour is deterministic. Agents in a MAS are AI agents that reason about their goals. They can plan, adapt, choose tools as they go and handle inputs they have not seen before. A microservice runs a fixed set of rules. An AI agent can reason about edge cases, ask for clarification and explain its decision. The two work well together. A MAS can call microservice APIs as tools.
SynaptxCloud designs and builds multi-agent systems in which specialised AI agents work in parallel. We design them to handle complex citizen services and back-office workflows.