# Multi-Agent Systems: What They Are, in Plain Language, in 2026 > Multi-agent systems in plain language: how several AI agents differ from one, how an orchestrator and workers operate, and when the combination pays off. Source: https://ailoot.ai/blog/en/multi-agent-systems/ Canonical: https://ailoot.ai/blog/en/multi-agent-systems/ Language: en Publisher: Ailoot (https://ailoot.ai/) Translation: guide-180 Revision: sha256-7362337dc6c563e851bbfb9af2cfd8c17d627ec749ec99ef7eb4537190b880da A year ago, working with AI followed the pattern “one chat, one agent, one task at a time.” Then, on complex projects, the agent began calling in helpers: one digs through documentation, another checks the figures, and a third pulls everything into one answer. That is a multi-agent system, and the term sounds much scarier than what lies behind it. Below, I explain in everyday terms what it is, how several agents differ from one, how they divide a single task, and when this combination really pays off. No formulas, with everyday examples and drawing on Anthropic engineering articles and Claude Code documentation.
[Ailoot documentation](https://ailoot.ai/docs/) — reference materials on using the service.
## What is a multi-agent system, in plain language?
Key takeaway A multi-agent system is several AI agents working together on one task. One plays the senior role: it breaks the task into parts and assigns them to the others. Each helper handles its own part separately and returns the result. The senior agent brings the pieces together into one answer. Essentially, it is a team of several AIs instead of one worker.
Imagine renovating an apartment. You can hire one contractor who drills channels in the walls, lays tile, paints, and installs outlets, one job after another. Or you can bring in a crew: the foreman assigns the work, the electrician runs the wires, the tiler lays the tile, and all of this happens at the same time. A multi-agent system works like a crew, except that instead of people, it has AI agents. An agent here is not just a chat that writes an answer. It is an AI that can use tools on its own: search the internet, read and edit files, and run commands. When several such agents work on a shared task, you get a multi-agent system. Anthropic’s wording in its engineering article is exactly this:

A multi-agent system consists of several agents — language models that autonomously use tools in a loop — working together.

— Anthropic, engineering article “How we built our multi-agent research system,” https://www.anthropic.com/engineering/multi-agent-research-system
The term is neither new nor marketing jargon; behind it is a clear engineering idea: divide the work among several workers. ## How does a multi-agent system compare with a single AI agent? {#how-is-a-multi-agent-system-different-from-one-ai-agent}
Key takeaway One agent holds the whole task in one head and works through the steps in sequence. A multi-agent system hands parts of the task to several helpers, who work at the same time, each in its own workspace. The upside is speed and breadth on large tasks. The downside is higher cost and more difficulty bringing everything together. One agent is enough for a simple question; a crowd only gets in the way.
The main difference is how the work and memory are distributed. A single agent handles the whole task in one conversation: every new step goes into the same context, and by the end of a large task it is already a jumble of logs, file snippets, and interim conclusions. A multi-agent system spreads this across different agents, each with a clean slate. I’ve put the differences in a table so they’re easy to see: | Feature | One agent | Multi-agent system | |----|----|----| | How the work proceeds | Step by step, in sequence | Parts of the task run simultaneously | | Memory and context | Everything in one conversation | Each agent has its own clean context | | Speed on a large task | Lower; everything is sequential | Higher thanks to parallel work | | Cost | Basic | Considerably higher (I’ll explain below) | | When it’s better | Simple question, short task | Large research project, many sources | | Risk | Gets bogged down in its own context | Harder to bring the pieces into one answer | The takeaway from the table is simple. A multi-agent system is not an upgrade to a regular agent for every situation. It is a separate tool for large tasks. For a short question, it is excessive and just burns money. For a broad, sprawling task with a dozen sources, it can handle what would bog down a single agent. ## How does the “orchestrator and workers” setup work?
Key takeaway This is the most common multi-agent system setup. There is a lead agent — the orchestrator. It receives a task, works out a plan, breaks the task into parts, and assigns them to helper workers. Each worker handles its part in a separate workspace and sends the result back. The orchestrator brings everything together into a single answer. It is like a foreman and a crew: one plans and assigns, the others do the work.
This setup has a technical name: orchestrator and workers. Anthropic describes it this way:

In the orchestrator-workers setup, a central language model breaks a task into pieces on the fly, assigns them to worker models, and brings their results together.

— Anthropic, engineering article “Building effective agents,” https://www.anthropic.com/engineering/building-effective-agents
Here is what this looks like in a real example. In its research feature, Anthropic uses a lead agent that receives the user’s request, works out a strategy, and creates several helper agents. Each goes off to investigate its own part of the topic, while the lead waits and then pulls together what they found. Here is their own description of the process:

When a user submits a query, the lead agent parses it, develops a strategy, and spawns subagents to explore different aspects of the question simultaneously.

— Anthropic, engineering article “How we built our multi-agent research system,” https://www.anthropic.com/engineering/multi-agent-research-system
The key point is that the helpers work in parallel; that is the whole purpose of the setup. In the same system, the lead agent calls in three to five helpers at a time, and each of them also uses several tools simultaneously. This lets a large task that would stretch into a long sequence of steps for one agent collapse into several parallel streams for the crew. There are two related concepts alongside the “orchestrator and workers” setup that are often confused with multi-agent systems: 1. **Routing.** The system looks at an incoming request and sends it down the appropriate path: a question about payments goes one way, a technical question another. This is not yet a team of agents; it is sorting at the entry point. 2. **Prompt chaining.** The task is divided into steps, and each next step works with the previous step’s result. This is a pipeline with one agent; several agents are not working alongside one another here. A multi-agent system differs in that there really are several workers, and they work simultaneously, each in its own workspace. Context isolation is part of what I call context engineering: managing what gets placed in AI memory and where. ## Where are you already using multi-agent systems without knowing it?
Key takeaway If you work in Claude Code, you already have a multi-agent system at hand. It has subagents: the lead agent gives part of the task to a helper, which does it in a separate window and returns only the result. Claude’s research feature is multi-agent too. In other words, this is no longer a lab experiment: the same mechanics are running inside tools people use every day.
The closest example is subagents in Claude Code. Claude’s documentation describes them briefly:

Subagents are specialized AI assistants that take on specific types of tasks.

— Claude Code documentation, section on subagents, https://docs.claude.com/en/docs/claude-code/sub-agents
The key feature is a separate workspace. A subagent does not sit in your main conversation; it has its own context window, its own set of instructions, and its own permissions:

Each subagent works in its own context window, with its own system prompt, its own access to tools, and separate permissions.

— Claude Code documentation, section on subagents, https://docs.claude.com/en/docs/claude-code/sub-agents
In practice, it looks like this: you ask an agent, say, to analyze a large error report. It does not flood your chat with a hundred lines of logs. It hands that dirty work to a subagent. The subagent digs through the logs in its own workspace, then returns a short conclusion: the cause is this, fix it here. Your main conversation stays clean, and the AI does not start getting confused and losing the thread. This also makes it clear why this matters to business. I use Claude Code and other agents together, and break large tasks into subagents for exactly this reason: so each can dig into its own area without getting in the others’ way. If you want to understand what kinds of tasks are worth giving an agent, I have a separate article about [tasks for an AI agent](https://ailoot.ai/blog/en/business-tasks-for-ai-agents/). And for how many helpers to keep running at once without overloading the agent, I wrote about [subagents and Claude Code extensions](https://ailoot.ai/blog/en/claude-code-skills-subagents-mcp-plugins/). ## Why split a task across several agents?
Key takeaway Two reasons. The first is clean memory: each agent has its own context, so one does not drown in another’s clutter. The second is speed: the helpers work at the same time instead of one after another. On a large research project, this makes a noticeable difference. Anthropic’s multi-agent setup beat a single agent by 90 percent on its internal evaluation. But the gain appears only on tasks that can actually be divided into parts.
The first reason is memory. When one agent handles a large task, its context fills up quickly: search results go here, file snippets there, and interim reasoning above. By the end, it no longer remembers well where it started. Spreading this across several agents means giving each a clean slate. Anthropic describes the benefit this way: each subagent provides division of labor with its own tools and its own line of search, making the research more comprehensive. The second reason is speed and breadth. Five helpers digging at the same time cover a topic more broadly and quickly than one agent working through it in sequence. Anthropic got this result in its internal research quality evaluation:

We found that a multi-agent system with Claude Opus 4 as the lead agent and Claude Sonnet 4 subagents outperformed a single Claude Opus 4 by 90.2% on our internal research quality evaluation.

— Anthropic, engineering article “How we built our multi-agent research system,” https://www.anthropic.com/engineering/multi-agent-research-system
It is an impressive figure, but it comes at a cost, and being honest about that cost matters more than the gain itself. I’ll cover it separately — this is where expectations often fall apart. ## Why does a multi-agent system burn 15 times more tokens?
Key takeaway Each helper is a separate AI that reads its part of the task and writes its own answer. They all spend tokens, and the total adds up. According to Anthropic’s measurements, one agent uses about four times as many tokens as a regular chat, while a multi-agent system uses about fifteen times as many. Speed and breadth cost money. So this setup pays off only on high-value tasks.
A token is the unit used to measure AI usage: roughly, a piece of text that you pay for. One agent already uses more than a regular chat because it does more than answer: it runs tools, reads files, and searches. When there are several agents, their usage adds up. Anthropic’s measurement, based on its own data:

Our data show that agents typically use about four times more tokens than chat, while multi-agent systems use about fifteen times more tokens than chat.

— Anthropic, engineering article “How we built our multi-agent research system,” https://www.anthropic.com/engineering/multi-agent-research-system
That leads directly to a conclusion about money. Fifteen times the usage makes sense only where the result is worth that much. Anthropic puts it plainly: a multi-agent system is justified for tasks whose value is high enough to pay for the increased usage. For a short question that one agent could handle, bringing in a crew means paying for five people where one would do. This same mechanism has an upside too. Anthropic observed that token usage itself explains much of the difference in quality: the more a system can spend on a task, the better the result. In other words, a multi-agent system often wins simply because it lets you put more compute into the task. If your budget is limited, that same logic works against you. I have an article about [how not to burn through your limits in Claude Code](https://ailoot.ai/blog/en/ai-agent-burned-budget-claude-code/). ## When is a multi-agent system justified, and when is it not?
Key takeaway It is justified when a task is large, can be divided into independent parts, and its result is valuable: broad research, gathering data from many sources, or analyzing a large volume of material. It is not justified when a task is small, proceeds strictly step by step, or its parts depend on each other. A classic example where multi-agent systems struggle is most coding work: the steps do not divide well into parallel tasks.
A simple rule: the bigger and broader the task, the more sense a crew makes. Anthropic gives a clear scale-based breakdown: | Task type | Number of agents | Example | |----|----|----| | Simple fact lookup | 1 agent, 3-10 tool calls | “Find the release date of a certain model” | | Direct comparison | 2-4 subagents, 10-15 calls each | “Compare three tools by price and features” | | Complex research | More than 10 subagents with clearly defined areas | “Build a complete picture of the market on a topic” | Anthropic also says directly where multi-agent systems do not help. Not every task can be broken into parallel parts, and agents are not yet very good at coordinating with each other on the fly:

For example, most coding tasks have fewer truly parallel parts than research, and AI agents are not yet very good at coordinating and delegating work to each other in real time.

— Anthropic, engineering article “How we built our multi-agent research system,” https://www.anthropic.com/engineering/multi-agent-research-system
The takeaway is simple. If you are writing code where each step follows from the one before it, a crowd of agents is more likely to get confused than to help. If you are assembling broad research from a dozen sources, a crew can handle what would overwhelm one agent. And Anthropic’s general advice, which it repeats separately, is to start with the simplest solution and add complexity only when you hit a limit.

We recommend finding the simplest possible solution and adding complexity only when necessary. Sometimes that means not building an agent system at all.

— Anthropic, engineering article “Building effective agents,” https://www.anthropic.com/engineering/building-effective-agents
## How do you put together your first multi-agent setup?
Key takeaway Start with a task that can really be divided into parts. Architecture is a minor detail here. Then go from simple to complex: first one agent, then one subagent for the dirty work, then several in parallel. In Claude Code, you do not need to program anything for this: subagents are set up through configuration. I’ve laid out the steps below in the order I use when setting up a system on a new project.
In my experience, the order is: 1. **Choose a task that can be divided into parts.** Broad research, gathering data from many places, or analyzing a large volume of material. If the task proceeds strictly step by step, you do not need multi-agent setup; stick with one agent. 2. **Start with one agent.** Run the task as usual. Often, it turns out that one is enough and there is no need to build a crew. 3. **Move the dirty work to one subagent.** When you see that part of the task is flooding the context with logs or search results, hand it to a subagent. In Claude Code, this is done through configuration, without code: the subagent gets its own window and its own set of instructions. 4. **Add parallelism when there are many parts.** Several subagents, each responsible for its own part of the topic, work simultaneously. Give each one a clearly defined area so they do not interfere with one another’s work. 5. **Keep an eye on usage.** Remember the fifteen-fold difference. If the task is not worth that cost, go back to one agent. If you are not working in Claude Code, the same mechanics apply in other tools. I have a general [map of vibe-coding tools](https://ailoot.ai/blog/en/claude-code-alternatives/) on what to use for coding and running agents in 2026, and an article about [nested subagents](https://ailoot.ai/blog/en/claude-code-nested-subagents/) on delegating a large task to nested helpers. ## What else do people ask about multi-agent systems?
### Frequently asked questions Is a multi-agent system always better than one agent? No. For a small task or one where steps must happen strictly in sequence, one agent does a better job and costs less. Multi-agent systems win only on large tasks that can be divided into independent parts, and they cost considerably more: according to Anthropic’s measurements, about fifteen times as many tokens as regular chat. Do you need to program to put together a multi-agent system? Usually not. In Claude Code, subagents are set up through configuration: you describe what the helper does, and the lead agent assigns suitable tasks to it. The helper has its own context window and permissions. No code is required. Are an orchestrator and a lead agent the same thing? Yes, they are different words for the same role: the lead agent that receives a task, breaks it into parts, assigns them to helpers, and brings their answers together into one result. In English, this setup is called orchestrator-workers. How is a multi-agent system different from a business run by one person with AI? A multi-agent system is a technical setup: several AIs work on one task. A business run by one person with AI is about business: one person runs an entire company with the help of AI. Multi-agent systems can be a tool within such a business, but they belong to different levels of discussion: one is about how AI is structured, the other about how a business is structured. Where should I start if I want to try multi-agent systems? Start with one large task that can be divided into parts, and one subagent for the dirty work. Do not bring in five helpers right away. First see whether one agent can handle it, then move the part that is flooding the context to a separate helper, and only then add parallelism.
### Sources - [Anthropic, “How we built our multi-agent research system” (June 13, 2025)](https://www.anthropic.com/engineering/multi-agent-research-system) - [Anthropic, “Building effective agents” (December 19, 2024)](https://www.anthropic.com/engineering/building-effective-agents) - [Claude Code documentation, section on subagents](https://docs.claude.com/en/docs/claude-code/sub-agents)
More materials are available on [Ailoot](https://ailoot.ai/). ## Questions about Multi-Agent Systems: What They Are, in Plain Language, in 2026 ### When should I keep a task with one agent? Keep one agent when the task is short or each step depends on the previous result. Parallel helpers are useful when valuable work can be divided into independent parts; measure their combined usage as well as the time saved. ### What is the smallest useful first multi-agent setup? Run the real task with one agent first. If logs or searches overwhelm the main context, move that part to one helper with a clear scope and ask for a concise result. Add more helpers only after that split proves useful. ## Ailoot · The company and its project portfolio Ailoot combines established operating experience, a large client base, stable service and ongoing KPI control. The company and its project portfolio: millions of customers; monthly sales in the billions. Ailoot brings that operating experience to API access for AI features in customer products, agents, applications and automations.