Which tool to choose
- You want to try the concept without programming: you simulate multiple agents inside a single assistant, assigning it different roles in turn ("now you're the researcher, then you'll be the reviewer"). It's the simplest way to understand how tasks are divided.
- You want separate agents that pass the work to each other automatically: a no-code platform (Make, with its map-based logic) connects multiple AI steps in sequence, each with its role.
- You want a custom system with autonomous agents that talk to each other: there you need to program, and it's advanced terrain; for most practical uses it's overkill.
How to do it
The principle: one agent, one trade. Collaboration is born from the clear division of roles, not from having lots of generic agents.
- Break the goal into distinct roles, not into random steps. Example for an article: one agent gathers the sources, one writes the draft, one is the critical reviewer. Each role has a task the others don't touch.
- Define what passes from one to the other. Collaboration lives in the "handing off": the researcher's output is the writer's input. The operational syntax for the handoff:
You are the reviewer. You receive the draft produced by the writer. Your only task: flag factual errors, unclear passages and statements without a source. Don't rewrite, don't add content: give back a list of corrections to the writer.
- Put a coordinator that directs the traffic. It can be an agent that decides who works next, or more simply you, passing one's output to the next while checking at each handoff.
- Insert a human verification point before the final result. Multiple agents that trust each other propagate an error without anyone stopping it: the last check is yours.
- Keep track of who did what. When the final result is wrong, you have to be able to trace back to the agent that introduced the error, otherwise you correct at random.
A concrete example
A small editorial team wants to produce cards on upcoming books. It sets up three roles. The researcher gathers plot, author and reviews and hands them over in a data card. The writer turns the card into an engaging text. The reviewer checks that the text contains no errors against the card and flags two points where the writer had exaggerated a review. The coordinator (a human editor) reads the final version before publication and cuts a sentence that's still too enthusiastic. Three agents did the bulk, but the typical error (inflating the reviews) was stopped first by the reviewer and then by the human. Without those two filters, the chain would have published an exaggeration with the utmost confidence.
When it does NOT work (and how to fix it)
If the agents pass on an error without noticing
Each agent trusts what it receives from the previous one, so an initial error arrives intact all the way to the end. Remedy: give one agent the explicit role of checker and, above all, put the human verification at the end. Blind trust between agents is the structural weak point.
If the roles overlap and do the same thing
Two agents with vague tasks end up duplicating or contradicting each other. Remedy: write for each one what it does and what it must not touch. Clear boundaries are the difference between collaboration and confusion.
If the system becomes too complex to follow
More agents mean more breaking points and more things to supervise. Remedy: start from two roles, not five. Add an agent only when the existing ones work and the gain is clear. Complexity is always paid for in control.
A tip from someone who actually uses it
Ask yourself whether you really need multiple agents or whether a single one, well instructed in phases, would suffice. Often collaboration between agents is a fascinating solution to a problem that a single agent guided step by step would solve with half the risk. Use multiple agents when the roles are genuinely different, not for fashion.
Frequently asked questions
Do multiple agents work better than one alone?
Only when the tasks are genuinely distinct and separable. For many jobs a single agent guided in phases is simpler to control and makes fewer mistakes. Multiple agents make sense when the division of roles reduces errors, not when it only adds pieces to supervise.
Do you need to program to make agents collaborate?
For custom systems yes, but you can try the concept without code: by assigning roles in turn inside an assistant, or by chaining AI steps on a visual platform. Programming is only needed when you want autonomous agents that talk to each other on their own.
Is it true that a multi-agent system self-corrects?
It's the illusion that makes you skip the final check. Agents can correct each other only on what they're instructed to look for; an error that no role is tasked with noticing passes undisturbed. Self-correction between agents reduces some errors and hides others: human control at the end isn't optional.