Multi-agent systems, explained for business owners
“Multi-agent” is one of the most used phrases in AI right now, and one of the least explained. Here is what it means in plain English, when it is the right design, and when a simpler system will do the job for less.
Harnad AI · · 6 min read
An AI agent, in one paragraph
An AI agent is a system that can work out the steps of a task and take actions using tools you allow it to use: reading a document, looking up a record, booking a slot, sending a message. Unlike a chatbot that only replies, an agent completes something. Good agents work within limits, keep a record of what they did, and hand over to a person when they are unsure.
From one agent to several
A multi-agent system splits a process into stages and gives each stage to a specialised agent. An orchestrator passes work between them, keeps the order, retries what fails, and stops at checkpoints for a person to approve. It is like a small team where each member has one job, rather than one person doing everything.
A worked example
Take a weekly video content process. A research agent finds and scores topics and lists its sources. A scripting agent turns the chosen topic into a draft in the brand’s voice. The draft waits for a person to edit and approve it. Only then does a production step render the video and queue it for publishing. Each agent is simpler and easier to test than one agent trying to do everything, and the approval checkpoint keeps a person in charge of what goes out.
When you need one
- The process has distinct stages that need different skills
- Work runs in parallel or across several systems and roles
- Each stage needs its own checks, and some need sign-off
- Failures must be retried or escalated without losing work
When you do not
More agents means more model calls, more to monitor, and more to maintain. Use the simplest system that does the job.
- The task needs one main skill, such as answering questions or booking appointments
- Steps follow a fixed order that ordinary software can handle
- Running cost and simplicity matter more than covering every edge case
What to ask before you buy one
- Why does this process need several agents rather than one?
- Where are the human checkpoints, and who approves what?
- How is each agent’s output evaluated and monitored?
- What does one run of the process cost, and how is it measured?
- What happens when a step fails?
- Who owns the code, the prompts, and the data?
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