What is a multi-agent system?
A multi-agent system is several AI agents, each with a defined job, working together on a larger task than one agent could handle well alone. A common design has a lead agent that plans the work, splits it into pieces, sends each piece to a specialist agent and combines the results. Each agent gets its own instructions, tools and data.
Why it matters to a revenue leader
The logic is the same as building a sales team: specialists beat one generalist when the work is broad and can be split. The costs are familiar too. Anthropic reported that its multi-agent research system used about 15 times the tokens of a chat interaction, and said the approach fits poorly when agents need the same context or depend heavily on each other. Microsoft's guidance adds that a single agent with tools is often the right default. A leader should ask for the business case the same way they would for a new hire, starting with what the extra role produces that the current setup cannot.
Example
A testing and inspection company preparing for a large account review sets up a lead agent that splits the job. One agent reads three years of work orders, another summarizes open deficiency quotes, and a third reviews contract terms. The lead agent combines their findings into a brief that the account manager edits before the meeting.
"Would one well-built agent do this job, or do we need a team of them?"
Sources
- How we built our multi-agent research system checked 9 Oct 2026
- AI Agent Orchestration Patterns checked 9 Oct 2026