Multi-agent system
A multi-agent system is a setup in which several AI agents, each with its own role, instructions, and tools, work together on a task. Typically an orchestrator agent breaks the work into parts and hands them to specialist agents, then combines or checks their results. It trades simplicity for parallel work and separation of duties.
01why it matters for a business
Some jobs are too broad for one prompt and one set of tools. Researching a market, reviewing a large contract set, or migrating a codebase involves many independent subtasks. Splitting them across agents lets the work run in parallel, keeps each agent's context focused on its piece, and lets you give different agents different permissions: the agent that reads customer data does not need the ability to send email.
The cost is complexity. More agents mean more model calls, higher inference cost, more places for errors to compound, and harder debugging. A single well-built agent with good tools beats a team of agents for most business processes. Multi-agent designs earn their keep when the work is genuinely parallel or when separating duties is a control requirement.
02what it looks like in practice
A private equity team wants a first-pass diligence summary of a target's contracts. An orchestrator agent inventories the data room and assigns each contract to a reviewer agent instructed to extract term, renewal, change-of-control, and exclusivity clauses. A separate checker agent samples the extractions against the source text and flags disagreements. The orchestrator assembles a table with links back to each clause, and an associate reviews the flagged items rather than reading every page.
The reviewer agents only have read access to the data room. Only the orchestrator writes the final output, and nothing leaves the workspace without a person.
03common mistakes
- Reaching for multiple agents because it sounds advanced. Start with one agent and split only when a measured limit (context, speed, permissions) forces it.
- No shared definition of done. Each agent needs an explicit output format so the orchestrator can check and merge results.
- Letting agents call each other without limits, which can loop and burn budget. Cap steps and spend per run.
- Testing only the final answer. Evaluate each agent's role separately so you know where errors start.
04related terms
- AI agentAn AI agent is software that uses a language model to pursue a goal by choosing its own next steps: it reads the situation, picks a tool or action, checks the result, and repeats until the task is done or it needs a person.
- AI orchestrationAI orchestration is the coordination layer that decides which model, tool, data source, or agent handles each step of a task, in what order, and what happens when a step fails.
- Agentic workflowAn agentic workflow is a business process in which an AI model handles some of the steps itself, deciding how to complete them, while the overall sequence, rules, and handoffs are defined in advance.
- Inference costInference cost is what it costs to run an AI model to produce outputs, as opposed to the cost of training it.
- AI evaluations (evals)Evals are structured tests that measure how well an AI system performs on a defined task, using a set of real or realistic inputs with known good outcomes.
05where insomnia club fits
Insomnia Club builds single and multi-agent systems, and will tell you when one agent is enough. Architecture, permissions, and cost per run are settled during scoping, before a fixed budget is agreed.
see AI agent development →tell us what keeps you up at night.
Scoped by the people who ship it. Priced before we start.
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