Key takeaways
- Each agent in a multi-agent system has a narrower role than a single all-purpose agent.
- Coordination comes from rules or from an orchestrating agent.
- Specialisation and parallel work are the gains; errors passed along and harder tracing are the costs.
- More agents mean more actions to oversee, so approvals and logs matter more.
How a multi-agent system divides work
A multi-agent system divides work by giving each agent one part of the task and a way to hand its result on. One agent might gather information, another write, another check; each has the instructions and tools for its part only.
Agent orchestration is the job of deciding who does what and in which order. It can be a fixed pipeline set by a developer or an orchestrating agent that reads the goal, assigns steps and collects the results.
Where multi-agent AI comes from
Multi-agent AI comes from a long line of research on agents that cooperate, negotiate or compete, covered in standard AI textbooks well before today's language models. That work studied how independent agents share information, divide tasks and reach agreements.
Language models have revived the idea. It is now practical to give each agent a role in plain words, a set of tools and a model to reason with, then let them pass work between them.
Multi-agent vs single agent
A single agent handles a whole task itself, while a multi-agent system splits it between specialists. A single agent is simpler to build, cheaper to run and easier to trace, and it is often enough.
Splitting pays off when parts of a task need different instructions, tools or permissions, or when they can run at the same time. A research agent can be allowed to browse while a writing agent is not, which narrows what each can get wrong.
Risks of multi-agent collaboration
Multi-agent collaboration adds its own risks on top of those of a single agent. Each handoff is a point where context can be lost or a mistake passed on.
- Errors passed along: a wrong fact from the first agent shapes every later step.
- Harder tracing: working out which agent caused a problem takes more effort.
- More actions to oversee: several agents can produce more sends and writes than one.
- Higher cost: more agents usually means more model calls for the same task.
Multi-agent system examples in sales and marketing
In an illustrative sales case, a research agent profiles a target account from its website and recent news, a writing agent drafts outreach in the company's brand voice from that profile, and a person reviews the draft before anything is sent.
The split keeps each step focused: the research agent cites what it found, the writer works only from that profile, and the reviewer can see where each claim came from. The person stays responsible for what goes out.
Where Pebbles Ai fits
- CapabilityAI workspacePebbles Ai has three assistants, the Marketing and Strategy assistants and the Guider, the in-app guide, and one of them can pass a piece of work to another by delegating it in the chat.
- FoundationIntegrationsActions that reach the outside world, such as sends, CRM writes and MCP calls, follow the execution mode: in Manual they wait for approval, in Plan they are queued and in Auto they run, apart from template send and calendar delete, which always ask.
Common questions
What is an example of a multi-agent system?
A research agent gathers facts about an account, a writing agent drafts an email from them, and a checking agent or a person reviews the draft before it is sent.
What does an orchestrator agent do?
It reads the overall goal, splits it into steps, assigns each step to the right agent and combines their results.
When is a single agent better than several?
When the task is short, needs one set of tools and permissions, and must be easy to trace. Adding agents adds handoffs and cost.
About this page. Written by the Pebbles Ai team for people who run sales, marketing and growth. Last updated .
