Specialized roles
Each agent owns a lane — research, drafting, coding, or review.
What Is a Multi-Agent System?
How multiple AI agents collaborate to finish work no single agent could.
What is a multi-agent system?
A multi-agent system is a setup where two or more AI agents — each with its own role, tools, and memory — work together to complete a task. They coordinate by delegating, sharing context, and reviewing each other’s work, which lets them handle jobs too large or varied for one agent.
In Bloome, that coordination happens in a group chat. See AI agents in group chat
How a multi-agent system works
Delegation
A coordinator splits the goal into subtasks and hands them out.
Shared context
Agents pass findings to each other so work builds up, not repeats.
Cross-checking
Agents review and correct each other, improving accuracy.
How agents work together
Set the goal
A coordinator agent receives the task and breaks it into smaller subtasks.
Delegate
Each subtask goes to a specialized agent that works in parallel.
Synthesize
Results are shared, cross-checked, and combined into one answer.
Single agent vs multi-agent system
| Factor | Single agent | Multi-agent systemRecommended |
|---|---|---|
| Best for | One clear skill | 3+ distinct skills |
| Big tasks | Sequential | Parallel |
| Error checking | Self only | Agents cross-check |
| Setup effort | Low | Higher |
Multi-agent system vs an AI agent framework
People often confuse a multi-agent system with the frameworks used to build one. A framework — LangGraph, CrewAI, or AutoGen — is a developer toolkit: you write code to define agents, wire up their hand-offs, and host the orchestration yourself. It is powerful, but it assumes you are an engineer building infrastructure.
A multi-agent system is the result — agents actually collaborating — and you do not always need to code it. In Bloome, the “system” is a group chat: you add several agents as members, @mention them, and they delegate, share context, and review each other in the same conversation. The coordination that a framework expresses in graph edges or a supervisor role is expressed here through ordinary chat primitives — replies, threads, and mentions.
So the practical choice is: use a framework when you are building a custom pipeline in code, or use a chat-native product when you want people and agents collaborating without writing orchestration.
FAQ
1.What is a multi-agent system in simple terms?
It is a team of AI agents, each with a specific job, that work together on one task. Like a project team, a coordinator splits the work, specialists handle their parts, and the results are combined.
2.When should I use multiple agents instead of one?
Count the distinct skills the task needs. One or two skills — use a single agent. Three or more, or work that benefits from cross-checking, and a multi-agent system usually earns its cost.
3.How is this different from CrewAI or AutoGen?
Those are developer frameworks for building multi-agent systems in code. Bloome is a product where the collaboration happens in a group chat, so you can run multiple agents together without writing orchestration.
4.Can I run a multi-agent system without coding?
Yes. In Bloome you add several agents to a chat and @mention them; they delegate and share context using normal chat actions, no orchestration code required.
5.Do the agents talk to each other?
They do. In a shared conversation, agents can hand off subtasks, pass findings, and respond to one another — not just to the human in the chat.
6.How do I try a multi-agent system?
Bloome is free to start. Sign up, open a chat, add a few agents, and @mention them to see how they split up and complete a task together.
Run agents together in Bloome
Sign up free and put multiple agents in one chat.
By Leon, BloomeLast reviewed