Beyond Prompt AI Studio

AI under the hood

When multiple AI agents work together

Module 10 showed what a single agent can do. For more complex tasks, more and more systems have several specialized agents work together instead of building one all-rounder.

Four examples – to remember it

Try it yourself: match the role to the task

Role

Task

Why use multiple agents at all?

A single agent tasked with researching, writing, checking, and coordinating quickly becomes confusing and error-prone. Splitting the work across several specialized agents – each with a clearly scoped role – makes the system easier to follow and easier to control.

Typical role division

Research agent

Gathers and checks information from approved sources.

Execution agent

Does the actual task – drafting text, updating a record, an email draft – based on the research gathered.

Critic agent

Checks the execution agent's result against set criteria before it moves on – a kind of built-in second opinion.

Coordinator agent

Distributes subtasks to the other agents and decides when the overall result is done.

The advantage: control instead of a black box

Because each agent has a clearly scoped role and its own tool permissions (see module 13, security in production), you can trace exactly which step did what – and where a human approval step should be built in, if in doubt.

Why this matters for you as a decision-maker

Multi-agent systems sound more complex, but they're often the more reliable choice compared to a single all-rounder agent – because the built-in check (the critic agent) catches errors more easily. In a vendor pitch that mentions "multiple agents," it's worth asking: which agent is allowed to do what, and who checks the result?

Key takeaways

  • Several specialized agents instead of one all-rounder make a system easier to follow and easier to control.
  • Typical roles: research agent, execution agent, critic agent, coordinator agent.
  • A critic agent acts like a built-in second opinion before a result moves on.
  • Clearly scoped roles and tool permissions per agent make control and approval steps easier (see module 13).
  • For a multi-agent pitch, ask: which agent is allowed to do what, and who checks the result?

Quick check: did it land?

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Why are tasks often split across multiple agents instead of one?

Want to find out if a multi-agent system makes sense for your process?