When one AI agent isn’t enough: how to split work across specialised agents, coordinate them reliably and keep costs and risks under control.
By Muzammil Shehzad
Most AI features start with a single model and a single prompt. Summarise this document. Answer this question. Draft this email. For focused tasks, that is exactly right. But as soon as a workflow involves research, decisions, tool calls and checking the result, one agent with one enormous prompt becomes fragile and hard to improve.
Multi-agent architectures split that work across several specialised agents that collaborate. Done well, they are more reliable, easier to test and cheaper to run. Done badly, they are slow, expensive and confusing. This guide explains when the approach makes sense and how to design it so it works in production.
An agent is a language model that can decide what to do next. It receives a goal, can call tools such as a database query, a search or an API, looks at the results and decides on the next step until the goal is complete. A multi-agent system is several of these, each with a narrow role, working together.
If none of these apply, a single well-designed agent with good tools is usually better. More agents means more moving parts.
A coordinating agent breaks the task into parts and delegates each part to a focused worker, then combines the results. This suits open-ended tasks such as researching a market, preparing a proposal or analysing a set of documents.
Agents run in a fixed order, each transforming the output of the previous one. For example: extract data from an invoice, validate it against business rules, then post it to the accounting system. Pipelines are predictable and easy to monitor, which makes them a good default for business processes.
One agent drafts and another checks the draft against explicit criteria, sending it back with feedback until it passes or escalates to a person. This pattern is valuable when quality matters, such as customer-facing messages, contracts or technical content.
Not every agent needs the most powerful model. Routing and classification can often run on smaller, faster models, while planning and complex reasoning benefit from larger ones. Mixing models this way can reduce cost significantly without hurting quality.
Test each agent on its own with a set of realistic examples and expected results, then test the whole system end to end. Keep the test set and run it whenever you change a prompt or a model. This is the only reliable way to know that an improvement in one place has not caused a problem elsewhere.
Consider inbound lead handling. One agent replies to the customer and gathers requirements. A second classifies the lead and scores it against your ideal customer profile. A third drafts a summary for the sales team and creates the record in the CRM. Each is simple, testable and replaceable, and together they deliver a fast, consistent response to every inquiry.
Start with the simplest design that works. Add agents only where they clearly improve quality, control or speed.
Map the workflow you want to automate, mark the steps that need judgement and those that don’t, and build the smallest version first. If you are planning an agentic system, our AI team can help you choose the right architecture before you invest in building it.
It is a design where several AI agents, each with a narrow role and its own tools, collaborate to complete a larger task, such as research, drafting and review.
No. For focused tasks a single agent with good tools is simpler and cheaper. Multiple agents help when a task has distinct stages, needs different permissions or requires independent review.
Use smaller models for simple steps, pass only necessary context, cache repeated results and set limits on iterations and spend per task.