Single-Agent vs Multi-Agent Systems: When Splitting the Work Is Worth It

Short answer: start with a single agent. Split the work across several agents only in four cases: the task has parts that can run in parallel, one context window cannot hold the work, parts need different tools or permissions, or the output needs an independent reviewer. If none of those four conditions is true, extra agents add cost, delay, and new ways to fail without improving the result.

The reason is cost. Anthropic reported that its agents use about 4 times more tokens than a chat conversation, and its multi-agent research system uses about 15 times more. That spending can be justified, but only when the task actually needs what several agents provide.

This article defines both designs, lists what a multi-agent system costs, names the conditions that justify the cost, and ends with a checklist you can apply to any task.

What is a single-agent system?

A single-agent system is one language model running in a loop. On each turn the model reads the task and everything that has happened so far, chooses the next step, calls a tool, reads the result, and repeats until the task is done. The list of steps and results that the model reads on every turn is called the context.

Everything lives in one place. The agent sees its own earlier reasoning, every tool result, and every mistake it has made, so it can correct itself without anyone passing it information. A coding assistant that reads files, edits them, runs the tests, and fixes the failures is a typical single agent.

What is a multi-agent system?

A multi-agent system splits one task across several agents, each with its own loop and its own context. Usually one agent, called the orchestrator, breaks the task into parts and sends each part to a worker agent. The workers run, often in parallel, and return short results that the orchestrator combines into the final answer.

The important difference is that the workers do not share a context. Each worker sees only the instructions the orchestrator wrote for it, plus whatever it finds while working. That separation is both the main benefit of the design and the main source of its problems.

What does a multi-agent system cost?

Every extra agent adds five kinds of cost. Some can be reduced with good design, but none of them disappear.

Tokens. Each worker needs its own instructions, its own tool results, and its own reasoning, and the orchestrator needs room to plan and to read every result. Consider a research task that a single agent finishes in 20 model calls with an average context of 15,000 tokens, which is 300,000 input tokens. The same task split across four workers, each making 10 calls at 12,000 tokens, plus 6 orchestrator calls at 20,000 tokens, uses 600,000 tokens. The work doubled in price before the answer improved at all.

Latency. Parallel workers can finish sooner than one agent working step by step, but only if the parts are truly independent. In the example above, at about 4 seconds per call, the single agent takes 80 seconds. The multi-agent version takes about 56 seconds: 8 seconds of planning, 40 seconds of parallel work, and 8 seconds to combine the results. If the workers must wait for each other, the time saving disappears and the planning time is added on top.

Coordination failures. Agents can misread their instructions, repeat each other's work, ignore what another agent reported, or stop too early. A study by Cemri and colleagues, "Why Do Multi-Agent LLM Systems Fail?", analyzed more than 1,600 execution traces and grouped the problems into 14 failure modes in three groups: unclear specifications, misalignment between agents, and weak verification of the result. The open-source frameworks they studied failed on 41 to 86.7 percent of tasks.

Context lost at each handoff. The orchestrator may hold 20,000 tokens of understanding about the task, but it writes each worker a brief of perhaps 300 tokens. Anything the brief leaves out, the worker does not know. A worker that was never told the user wants results from the last six months will return older results with full confidence.

Debugging. When a single agent fails, one trace shows every step in order. When a multi-agent system fails, the cause may sit in a brief the orchestrator wrote, in one worker's tool call, or in the step that combined the results. Each run needs tracing across every agent, which is extra infrastructure to build and maintain.

When is splitting the work worth it?

Four conditions justify the costs above. A task usually needs at least one of them, and the strongest cases have two.

1. The task has independent parts that can run in parallel. Research is the standard example. Comparing ten vendors means ten separate searches that do not depend on each other, so ten workers can run at once. Anthropic reported that its multi-agent research system, with a Claude Opus 4 orchestrator and Claude Sonnet 4 workers, scored 90.2 percent higher than a single Claude Opus 4 agent on its internal research evaluation. That result came from breadth-first questions, where the parallel searches were the point.

2. One context window cannot hold the work. A context window is the maximum amount of text a model can read in one call. If a task requires reading 40 long documents, a single agent fills its window and starts losing earlier material. Workers can each read a few documents in a fresh window and return a 500-token summary, so the orchestrator holds only the summaries.

3. Parts need different tools or different permissions. An agent that both reads untrusted web pages and has permission to send email is a security risk, because instructions hidden in a web page can trigger the email tool. Splitting the work lets one agent read the untrusted content with no dangerous tools, while another agent with narrow permissions acts only on a checked summary. Fewer tools per agent also improves tool choice, since a model picks correctly from 5 tools more often than from 50.

4. The output needs an independent check. A model that reviews its own work tends to accept its own reasoning. A separate reviewer agent with a fresh context and its own instructions, such as checking every claim against a source, catches errors that the writer agent cannot see. This works best when the review criteria are concrete.

Want to build these designs step by step? Grokking Agentic Design Patterns covers orchestrator workers, context-isolated sub-agents, and the reliability patterns that keep multi-agent runs from failing quietly.

When should you keep one agent?

Keep one agent when the steps depend closely on each other. In most coding tasks, a change to one file affects the next change, so every step needs the full picture. Anthropic noted that tasks where all agents must share the same context, or where agents depend heavily on each other, are not yet a good fit for multi-agent systems, and that most coding tasks have fewer parallel parts than research.

Keep one agent when the task is short. If a single agent finishes in 8 calls, no split can save much time, and the orchestrator alone may add 3 or 4 calls.

Keep one agent when consistency matters more than speed. Two workers that each write half of a report will choose different terms, formats, and assumptions. One agent writing the whole report produces one voice and one set of decisions.

Finally, keep one agent while you are still learning what the task needs. A single agent with good tools and good traces shows you where it struggles. Those traces tell you which part, if any, deserves its own agent.

Common multi-agent topologies

A topology is the way the agents are connected. Three cover most production designs.

• Orchestrator workers. One agent plans, sends parts to workers, and combines the results. It suits research, data gathering, and any task with many independent parts.
• Pipeline. Each agent completes one stage and passes its output to the next, for example extract, then analyze, then write. It suits fixed processes where each stage needs different instructions or tools.
• Writer and reviewer. One agent produces a draft and another checks it against stated criteria, returning it for revision until it passes or a retry limit is reached. A related variant, often called debate, has two agents argue opposite positions while a third decides.

The orchestrator workers design is the most common, and it is the one the four conditions above most often justify.

A decision checklist

Ask these questions in order. Stop at the first answer that settles the design.

1. Does a fixed sequence of steps solve the task? If yes, use a workflow, which is ordinary code calling a model at each step, and not an agent at all.
2. Can one agent with good tools finish it within one context window? If yes, use a single agent.
3. Does the task split into parts that can run without waiting for each other? If yes, consider orchestrator workers.
4. Do parts need different permissions, or does the output need an independent check? If yes, split along that line and no further.
5. Is the result worth roughly two to four times the token cost of a single agent? If not, return to a single agent and accept the slower or narrower result.

Every agent you add should map to one of these answers. If an agent exists only because the design looked cleaner on a whiteboard, remove it.

Single-agent vs multi-agent compared

The table compresses one rule: a multi-agent system is a way to spend more tokens to get parallel work, larger effective context, or separation of duties. When the task needs none of those three, a single agent is the better design.

Frequently asked questions

What is the difference between a single-agent and a multi-agent system? A single-agent system is one model running in a loop with one shared context. A multi-agent system splits the task across several agents, each with its own context, usually coordinated by an orchestrator agent that combines their results.

Are multi-agent systems better than single agents? Not in general. They perform better on tasks with independent parallel parts, such as broad research, and on tasks too large for one context window. On closely connected tasks, such as most coding, a single agent is usually cheaper and more reliable.

How many more tokens does a multi-agent system use? It depends on the design, but the increase is large. Anthropic reported that its multi-agent research system used about 15 times more tokens than a chat conversation, compared with about 4 times for a single agent.

Why do multi-agent systems fail? Most failures come from unclear instructions, agents that misunderstand or ignore each other, and weak checks on the final result. Research on more than 1,600 execution traces grouped these into 14 failure modes across those three areas.

What is the orchestrator workers pattern? One agent, the orchestrator, breaks the task into parts and sends each part to a worker agent with its own context. The workers run, often in parallel, and the orchestrator combines their short results into the final answer.

How do I decide between one agent and several in a system design interview? Start with a single agent and name the condition that would justify a split: parallel parts, a context limit, different permissions, or an independent reviewer. Then state the cost in tokens and failure points, and add only the agents that condition requires.

Related reading

• Design an AI Agent: The 7-Layer Blueprint
• The ReAct Pattern: How an Agent Reasons and Acts in a Loop
• Agentic Design Patterns: The Patterns Behind Production AI Agents
• How Do You Design a Multi-Agent System?

Ready to design agents that work in production? Grokking Agentic Design Patterns teaches when to split work across agents, how to write the briefs between them, and how to trace and test the whole system.