Most conversations about AI focus on a single model answering a single question. Ask it something, get a response. Useful — but limited.
Multi-agent systems change the model entirely. Instead of one AI trying to do everything, you have a team of specialised agents, each with a defined role, collaborating to complete complex work. Like a real team — but running 24/7, without email threads.
What is an AI agent?
An AI agent is an LLM with a purpose. It has:
- A system prompt that defines its role, personality, and constraints
- Tools it can call — web search, code execution, APIs, databases
- Memory of the conversation and context it’s working in
- A goal it’s trying to achieve
An agent doesn’t just answer. It reasons, plans, uses tools, and iterates until it reaches its goal or hands off to the next agent.
What makes it a multi-agent system?
A multi-agent system is what happens when you put several agents in a team structure. Each agent specialises. Each knows who it reports to, what it needs to produce, and who gets the output next.
Consider a Finance Intelligence team:
- Market Data Collector — scrapes prices, news, filings
- Sentiment Analyst — reads tone across sources
- Financial Modeller — runs the numbers
- Risk Assessor — flags the exposures
- Report Writer — synthesises into a structured brief
- Email Formatter — formats for client delivery
Each agent does one thing well. The team does something no single agent could: produce a professional-grade research report in minutes, not hours.
The three collaboration models
Not all agent teams work the same way. There are three main topologies:
Sequential
Agents work in a chain. Agent 1 finishes, passes output to Agent 2, and so on. Predictable, auditable, great for pipelines with a clear order of operations.
Group Chat
Agents collaborate simultaneously — sharing context, reasoning together, challenging each other’s outputs. Better for complex tasks that benefit from multiple perspectives before a conclusion is reached.
Concurrent
Agents run in parallel on different subtasks, then a synthesiser agent merges their outputs into a single result. Fastest for tasks where the subtasks are independent — research on multiple topics, analysis of multiple datasets.
When does a multi-agent system beat a single agent?
Use a single agent when:
- The task is simple and self-contained
- You need one pass — summarise this, translate that
Use a multi-agent team when:
- The task requires specialised knowledge in multiple domains
- Quality matters and cross-checking improves the output
- The work has clear sequential steps (research → analysis → writing)
- Speed matters and parallel execution helps
A useful rule of thumb: if you’d hire more than one person for a task, you probably need more than one agent.
The business case
The ROI of multi-agent systems isn’t just about speed — it’s about consistency and scale. A team of AI agents:
- Never forgets the process — every step runs exactly as defined
- Never gets tired — it runs the same way at 3am on a Sunday
- Never misses a step — there’s no “I forgot to CC finance”
- Scales horizontally — run one team or a hundred
A Finance team that screens market intelligence every week. An HR team that screens 200 CVs overnight. A content team that produces 10 researched articles before Monday. These aren’t hypotheticals — they’re what Agentivity users run today.
Getting started
You don’t need to build a seven-agent pipeline on day one. Start small:
- Pick one repetitive, time-consuming task your team does every week
- Break it into its natural steps (research, analyse, write, format)
- Map one agent to each step
- Choose a topology (sequential is usually the right start)
From there, you add agents, refine prompts, and expand to new workflows.
That’s the loop. And once you see the first team deliver its first output — exactly the way you designed it — the rest follows naturally.
Agentivity is an open-source platform for building and running multi-agent teams. Star us on GitHub or explore the use cases.