Multi-agent systems: when it makes sense to use more than one AI agent

The question of whether a company needs a single AI agent or would be better served by multi-agent systems made up of several collaborating agents comes up more and more often when designing AI solutions. It isn't a question of fashion or technological sophistication — it's a question of how complex the process being automated actually is, and how much risk the company is willing to carry. This article offers a simple decision framework to help distinguish situations where one well-configured agent is enough from situations where investing in orchestrating multiple agents pays off.

A single AI agent is essentially one LLM process with access to tools (a database, an API, email, a calendar) that receives a task, decides on the steps and carries them out from start to finish. It works well as long as the task is bounded and doesn't require markedly different types of reasoning.

Multi-agent systems, by contrast, split the work across several specialised agents — each with its own role, its own context and often its own set of tools. Above them typically sits an orchestrator (a controlling agent or a fixed workflow) that decides which agent gets assigned a task or part of one, and assembles the partial results into a final output. It's therefore an architectural decision, similar to whether a monolithic application should be split into microservices — the benefits come bundled with coordination overhead.

In most real-world business cases, a single agent isn't just sufficient — it's the right choice. The typical signs are:

• The task has one clearly defined input and one type of output — for example, answering a customer query, categorising an email, filling in a form.
• The agent works with a limited number of tools and integrations that don't overlap with one another.
• The process is linear, with no need to combine markedly different types of expertise at the same time (for example, legal analysis alongside technical API calls).
• Errors are easy to trace and easy to fix, because there's only one decision point.

If these conditions are met, a single well-designed agent with a clear role and a limited scope of authority will handle the task reliably without a complex architecture. Adding further agents here would only increase complexity without any real benefit — and in a business setting that means higher development and ongoing operational costs with no corresponding upside.

When the task requires several different types of expertise

If a process naturally combines multiple roles — for example, one agent analyses the input data, a second checks it against internal documentation and a third drafts the final response or document — it's more effective to give each role its own agent with a tightly focused prompt and toolset than to try to cram everything into one general-purpose agent. A specialised agent has a shorter, more precise context and typically makes fewer mistakes than an agent that has to handle everything at once.

When you need to parallelise or scale

Some tasks can naturally be split into independent parts that can run concurrently — for example, processing dozens of documents, where each one is handled by a separate agent and the results are merged at the end. Here a multi-agent approach doesn't just improve quality — it also speeds up processing of a larger volume of work.

When you need checking and verification

A common, proven pattern is the executor-agent and reviewer-agent pair: the first proposes a solution or carries out an action, the second independently checks it against rules, data or company policy before the result is confirmed or sent back for rework. This pattern is especially useful for higher-risk tasks — for example, working with financial or contractual documents — where a simple "four eyes" check significantly reduces the number of errors that would otherwise make it further down the process.

Orchestration means designing how agents communicate with each other and who decides the sequence of steps. In practice, two approaches come up most often:

• A controlled workflow — the sequence of steps is defined in advance (agent A always hands its output to agent B), which is predictable and easier to test and debug.
• A dynamic orchestrator — a central agent decides at runtime, on its own, which specialised agent to involve and in what order, which is more flexible but harder to design, monitor and debug.

How agents pass context to one another also matters — shared memory, structured messages, or a database holding ongoing state. Without careful design of this layer, a multi-agent system quickly becomes unpredictable. This is where it differs most from integrating a single agent — with one agent, the main task is connecting it to existing systems; with several agents, an entire additional layer of communication between the agents themselves comes into play.

The chart below is an illustrative model, not measured INTERFASE data — it's meant only to show the general principle that as task complexity grows, so does coordination overhead, and splitting the work across several specialised agents can partially reduce that overhead compared with forcing everything into a single general-purpose agent. The actual load always depends on the specific process and how it's designed.

A multi-agent architecture isn't automatically the better choice, and it comes with its own risks:

• Higher debugging complexity — when a result fails, you need to work out which agent in the chain made the mistake, and why.
• Risk of "lost information" when handing off context between agents, if the communication protocol isn't well designed.
• Greater demand for monitoring and logging — without visibility into what individual agents are doing, the system becomes a black box.
• Unnecessary complexity where a single agent would do — this is one of the most common mistakes companies make when deploying AI.

The same consideration — when to reach for a more complex tool and when a simpler solution is enough — is worth applying already at the design stage of custom software development itself. The principle is the same: the complexity of the solution should always match the complexity of the real-world problem, not the other way round.

Before deciding between a single agent and a multi-agent system, ask yourself a few questions:

1. Can the task be described in one sentence with one clear input and output, or does it combine several different types of decision-making?
2. Are there steps in the process that someone (or something) needs to independently verify before the final output?
3. Can parts of the task be processed concurrently, or must they necessarily happen one after another?
4. Do you have the capacity to monitor and debug a more complex system, or do you need something that's simple to maintain?
5. Will the scope of the task change in the near future enough that adding further roles would be a natural next step?

If most of your answers point to "yes, it's more complex and more varied", a multi-agent approach will likely deliver greater accuracy and reliability. If the task stays narrowly bounded, a single well-designed agent will be simpler to deploy and maintain — and in most business scenarios, that's exactly what pays off.

A detailed technical look at designing multiple collaborating agents is also available from Anthropic's own breakdown of its multi-agent research system, where they describe their own experience orchestrating agents in practice.

If you're weighing up whether your process needs a single agent or a more complex architecture, take a look at our AI and automation solutions or arrange a no-obligation consultation via contact — we'll go through your specific process together and design an architecture that matches its real complexity. You can also find examples of specific projects in our references.