The simplest architecture is one agent handling the full task: it receives a goal, reasons, calls tools, reads results, and continues until done. That is the sensible default. A multi-agent system adds a layer of coordination on top, distributing work across agents that each play a defined role. Whether that addition is worth it depends entirely on the structure of the task.
One model, one unbroken context
A single-agent system runs entirely within one conversation context. The model sees everything that has happened: what it found at step two, what it decided at step four, what the tool returned at step six. This continuity is a genuine advantage on sequential reasoning tasks, where each step builds directly on the last and where the full chain of evidence needs to be available at the point of conclusion.
The constraint is the context window. A task that exceeds what a single model can hold, or that spans genuinely separate knowledge domains, starts to strain this architecture. An agent asked to research a technical topic, generate a business summary, and draft a communication in a specific tone can handle all of that alone. But it is doing everything in one pass, with no specialization and no independent verification step.
Specialization by design
A multi-agent system decomposes a task across agents with distinct roles. A common pattern pairs a planner breaking the work into subtasks with a reviewer checking the output before it is returned. Each agent is focused on a narrower problem, and can be optimized or prompted for that specific scope.
Two structural patterns govern how agents are organized. In a flat architecture, agents operate as peers, passing information directly between one another with no central coordinator. In a hierarchical architecture, a higher-level orchestrating agent decomposes the problem and delegates to sub-agents. Chen et al. describe both in their 2024 survey of LLM-based multi-agent systems (arXiv:2412.17481), noting that hierarchical arrangements suit tasks with structured role specialization, while flat arrangements suit more dynamic, less predictable problems.
Parallelism is a further advantage. Independent subtasks can run concurrently across agents, reducing total elapsed time when the work genuinely splits into non-sequential parts.
Four axes that separate them
Simplicity. A single-agent system is easier to build and debug. A multi-agent system introduces coordination logic and multiple points of failure.
Token consumption. Every inter-agent handoff requires re-stating context. Each agent needs enough background to do its job, and that context is duplicated across agents rather than shared within a single window. Research on LLM-based multi-agent systems consistently finds token consumption several times higher than in equivalent single-agent runs.
Latency. On tasks a single agent could complete directly, coordination adds round-trip time between agents. This overhead appears even when the quality of the output is identical or worse.
Accuracy. Multi-agent systems improve accuracy when subtasks are genuinely independent or when an independent reviewer catches errors a single agent misses. Single agents maintain an advantage on tasks requiring coherent, step-by-step reasoning where context from earlier steps shapes later decisions.
Each handoff is a compression step
The trade-off most comparisons understate: every time one agent passes its output to another, that output is summarized and passed along. The receiving agent reasons from a compressed version of what the sending agent found, not from the original. On tasks where the relevant detail is in the specifics rather than the summary, that loss accumulates across handoffs.
This is why multi-agent systems are not a better architecture in the abstract. They are a better architecture for tasks that decompose cleanly: where subtasks are independently meaningful, or where parallel execution genuinely cuts wall-clock time. Where those conditions are absent, the coordination overhead is a cost with no return.
For an AI agent already capable of multi-step, tool-using tasks, the threshold question before introducing additional agents is whether the task has structure that genuinely benefits from being split across separate contexts.
Sources
- Chen, S. et al. A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application. arXiv:2412.17481, December 2024.
Questions, answered
When should you use a single agent instead of multiple agents?
When the task fits within one context window, requires coherent step-by-step reasoning, or does not benefit from parallel execution. Single agents are simpler to build and debug, and they outperform multi-agent setups on sequential reasoning tasks where early context shapes later decisions.
What is an example of a multi-agent system?
A system handling a complex research brief might use a planner agent to decompose the goal and one or more specialist agents to execute parts of it. Each agent operates within its own context and passes structured results to the next.
Do multi-agent systems always produce better results?
No. Multi-agent systems outperform single agents on tasks that decompose cleanly across independent subtasks. On sequential reasoning tasks, where each step builds on full context from all earlier steps, a single unbroken context consistently performs better because no information is lost at handoffs.
What is the main cost of a multi-agent system?
Token consumption and latency. Every agent needs enough context to do its job, and that context is re-stated at each handoff. Research on LLM-based multi-agent systems finds token consumption several times higher than in equivalent single-agent runs, even on tasks where the quality gain is modest.
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