The architecture for tasks that are structurally too large for a single agent, and the expensive misunderstanding about what adding agents actually buys you.
One agent can do a lot, but some tasks will always defeat it. Not because the model is weak, but because the task is structurally too large for a single mind. Multi-agent systems exist for exactly that reason.
The concrete problem: research a topic, write a report, fact-check it, translate it and publish it. One agent doing all of that sequentially in a single context runs into context limits, attention limits and specialisation limits.
A multi-agent system is an architecture where several agents work in coordination, each responsible for a specific subtask, to complete a goal that exceeds what a single agent can hold. One orchestrates, the others execute, and results flow between them.
The law firm analogy carries it. Not one lawyer doing everything. A partner orchestrates the case, one associate does legal research, another drafts contracts, a paralegal handles documents. Defined roles, structured handoffs, an outcome none of them could deliver alone at that speed and quality.
In practice an orchestrator receives the high-level goal and breaks it into subtasks. Sub-agent A searches, B summarises what was retrieved, C fact-checks against known sources, D formats the output. Each runs in its own context, no single agent sees everything, and the orchestrator assembles the result.
And now the correction that saves money. More agents is not more intelligence. More agents means more complexity, more failure points, more latency and more cost, because every agent call is an API call. A poorly designed multi-agent system is slower and more expensive than a well-designed single agent. You add agents when the architecture genuinely requires it, not to make something smarter.
| Role | What it does | Why it is separate |
|---|---|---|
| Orchestrator | Receives the high-level goal and breaks it into subtasks | Holds the plan so no single worker has to. |
| Sub-agent A | Searches for source material | Its context stays focused on retrieval. |
| Sub-agent B | Reads and summarises what was retrieved | Summarising is a different job from finding. |
| Sub-agent C | Fact-checks the summary against known sources | Checking your own work is the weakest link. |
| Sub-agent D | Formats the final output | Formatting does not need the research context. |
| Cost | Every agent call is an API call | More agents means more latency and more spend. |
| Failure mode | Bad output is inherited by the next agent | Errors compound and debugging gets harder with each one. |
A multi-agent system is an architecture where multiple AI agents work in coordination, each responsible for a specific subtask, to complete a goal that exceeds the capacity or scope of a single agent. One orchestrates, the others execute, and results flow between them.
When the task is structurally too large for a single context. Research a topic, write a report, fact-check it, translate it and publish it is the classic example. One agent doing all of that sequentially runs into context, attention and specialisation limits.
No, and this is the expensive misunderstanding. More agents means more complexity, more failure points, more latency and more cost, since every agent call is an API call. A poorly designed multi-agent system is slower and more expensive than a well-designed single agent.
Not in any meaningful sense. Every agent does what it was designed to do within the parameters you defined. They do not collaborate creatively, they follow routing logic and pass structured messages with outputs you specified.
Errors compound. One agent produces bad output, the next inherits it, and the problem grows as it travels down the chain. Debugging gets harder with every agent you add, so these systems need more careful design, testing and monitoring than single-agent setups.
AutoGen is Microsoft's open-source framework and handles the communication protocol between agents. In a no-code setting, n8n offers multi-agent workflow templates where each node is an agent or tool call and the orchestration logic is visible.
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One agent can do a lot, but some tasks will always defeat it, not because the model is bad but because the task is structurally too large for a single mind. Multi-agent systems exist for exactly that reason.
The concrete problem: research a topic, write a report, fact-check it, translate it and publish it. One agent doing all of that sequentially in one context hits context limits, attention limits and specialisation limits.
The definition. An architecture where multiple agents work in coordination, each owning a specific subtask, to complete a goal beyond the capacity of a single agent. One orchestrates, others execute, results flow between them.
The law firm. Not one lawyer doing everything: a partner orchestrates the case, an associate does legal research, another drafts contracts, a paralegal handles documents. Defined roles, structured handoffs, an outcome none could deliver alone at the same speed and quality.
How it works in practice. The orchestrator receives a goal such as researching EU AI regulation and writing a briefing, then assigns subtasks. One agent searches, one summarises, one fact-checks, one formats. Each runs in its own context, and no single agent sees everything.
AutoGen, Microsoft's open-source multi-agent framework, shows the conversation flow between agents. User proxy and assistant agent exchange messages and hand off tasks. It handles the communication protocol, which is infrastructure rather than magic.
The same idea without code. An n8n multi-agent workflow template shows each node as an agent or tool call, with outputs routed from one agent to the next and the orchestration logic visible on screen.
The expensive misunderstanding. More agents does not mean more intelligence. It means more complexity, more failure points, more latency and more cost, because every agent call is an API call. A poorly designed multi-agent system is slower and dearer than a well-designed single agent.
These systems are also not autonomous in any meaningful sense. Every agent does what it was designed to do within your parameters. They do not collaborate creatively, they follow routing logic and pass structured messages with outputs you defined.
How they fail. One agent produces bad output, the next inherits it, and the error compounds as it travels. Debugging gets harder with every agent added, so careful design, testing and monitoring matter more here than in single-agent setups.
The summary: split, specialise, orchestrate. Multi-agent systems are about better division of labour, not more intelligence, the same principle as any functional organisation.
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