Multi-Agent Systems: When Several Agents Beat One

One agent, several agents: what are we talking about

An autonomous AI agent runs on its own, with its own tools, memory, and goal. A multi-agent system, on the other hand, puts several agents to work on the same task, each with a specific role. The idea isn't new in distributed computing, but it has taken on concrete meaning since language models became capable of using tools and chaining steps without constant supervision.

The term covers different realities. There's the "orchestrator plus sub-agents" setup, where a main agent breaks the work down and delegates pieces to more specialized agents. There are also peer agents, which communicate with each other to share results without a strict hierarchy. The nuance to remember is that "several agents" doesn't mean "several identical instances doing the same thing in parallel": each agent has its own scope. Atako's glossary entry covers this distinction in more depth.

The principle of orchestration

What sets a genuine multi-agent system apart from a simple batch of scripts running in parallel is orchestration: the logic that decides who does what, in what order, and how the results get recombined. Anthropic has documented in detail the architecture it uses for its deep research feature: a "lead" agent analyzes the request, defines a strategy, then spins up sub-agents each tasked with exploring one angle in parallel before reporting their results back (source: Anthropic, "How we built our multi-agent research system," https://www.anthropic.com/engineering/multi-agent-research-system, accessed 2026-09-04).

This orchestrator-worker model shows up in almost every serious implementation. The main agent doesn't do the work itself: it plans, distributes, verifies, and assembles. This is what's known as agent orchestration. The sub-agent, for its part, is designed to be short-lived and disposable: it exists for the duration of a subtask, then it's gone.

Why several agents sometimes outperform one

The most cited result on this topic comes, again, from Anthropic. In its internal evaluation, a system made up of a lead Opus agent orchestrating Sonnet sub-agents outperformed a single Opus agent by 90.2% on the same research tasks (same source as above). The explanation comes down to one word: parallelization. A single agent explores leads one at a time, inside a single context window that eventually fills up. Several agents can explore several leads at once, each in its own context, before recombining what they found.

This advantage isn't universal. It shows up mainly on "breadth" tasks: document research, exploring multiple hypotheses, processing a large volume of disjointed information. LangChain, in a reference article on the topic, sums up the condition well: multi-agent systems excel at high-value tasks that parallelize well, and lose their appeal as soon as the work is heavily sequential or depends on shared context (source: LangChain, "How and when to build multi-agent systems," https://www.langchain.com/blog/how-and-when-to-build-multi-agent-systems, accessed 2026-09-04).

Anthropic went as far as statistically breaking down what explains performance gaps between configurations. Three factors alone account for 95% of the variance observed: the amount of tokens used, the number of tool calls made, and the model chosen. Token usage alone explains 80% of that variance (same source). In other words, it isn't "having several agents" that drives the performance gain, it's the capacity that gives you to burn more compute in parallel on a problem suited to it. On a problem that isn't suited to it, that same capacity is wasted, it simply costs more for a result comparable to a single agent's.

The price of coordination

This performance gain comes at a cost, and it's far from marginal. According to Anthropic, a single agent already uses about 4 times more tokens than a standard chat conversation. A multi-agent system uses about 15 times more (same source). That's not a minor accounting detail: at that level of multiplication, only tasks with genuinely high business value justify the investment.

The cost of coordination isn't limited to tokens. LangChain points to a more structural problem: concurrent writes cause far more trouble than concurrent reads. Two agents reading the same database in parallel isn't a problem. Two agents modifying the same document, the same ticket, or the same line of code at the same time produces conflicts that then need to be reconciled, often by hand. The more agents you add, the larger the failure surface grows: each additional agent adds a layer of shared state, a communication protocol, and a new point where the system can break without anyone immediately understanding why.

Debugging cost also has to be factored in. Following the reasoning of a single agent, with its timeline of actions, is already a job in itself. Following the chain of decisions between several agents splitting up a task, with their message exchanges and their occasional disagreements, requires much deeper observability. That's a project in its own right.

When not to go multi-agent

The "more agents, more power" instinct is one of the surest ways to blow through an AI budget without a proportional gain. Gartner has put a hard number on it: the firm predicts that more than 40% of agentic AI projects will be abandoned by the end of 2027, largely due to spiraling costs, poorly defined business value, and insufficient risk controls (source: Gartner, "Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027," https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027, accessed 2026-09-04). A good share of those failures comes from projects that add orchestration complexity the task never called for.

In practice, you're better off staying with a single agent when the task fits within a single context window, when it's heavily sequential (each step strictly depends on the previous one), or when the volume of work doesn't justify the token bill. LangChain cites coding as a telling example: unlike research, a development task rarely contains genuinely parallelizable work, which makes multi-agent setups less useful for this specific case than people tend to assume.

So the right question to ask before adding a second agent isn't "could this help?", it's "does this task have a real, natural dividing line?" If part of the work needs file access, another part needs database access, and a third needs a call to an external API, there's a genuine split available. If everything depends on the same thread of reasoning, adding agents mostly adds friction.

A simple checklist before you commit: does the task generate several independent leads that can be explored in parallel? Does the volume of work justify a token bill several times higher? Do the subtasks write to different systems, or do they risk stepping on the same data? If the answer is no to any of these three questions, a single well-equipped agent, with good access and clear instructions, will probably be faster and cheaper than a multi-agent architecture.

What Atako changes in practice

At Atako, this orchestrator/sub-agent logic exists natively: an agent can delegate a complex subtask to an ephemeral sub-agent, whose work rolls up as steps in the main agent's activity timeline, without consuming an extra slot. That avoids the most common pitfall: multiplying "full" agents (and their cost) when a simple temporary sub-agent would have done the job. If you want to dig into the budget side, the following article breaks down the real cost line items of an AI agent in a business, and the one on measuring ROI will help you check, once it's up and running, whether the investment was worth it.