Multi-Agent Systems › Why Multiple Agents?

Before diving into mechanisms, it is worth asking: when does a multi-agent setup help?

Three scenarios motivate the design:

• Parallelism: Long tasks can be decomposed into independent subtasks and executed simultaneously. Researching market trends for five industries at once is faster with five agents than with one running five sequential searches.
• Specialization: Different subtasks benefit from different prompts, tools, or even different models. A coding agent optimized for Python generation performs better at code than a general-purpose reasoning agent, while a document summarization agent excels at condensing long PDFs.
• Independent verification: Critical outputs benefit from a second opinion. Having an independent agent review generated code for correctness catches errors that the generating agent might miss, since the reviewer brings fresh context.

The cost is coordination overhead. Agents need to pass information between each other, wait for dependencies to resolve, and handle failures in other parts of the system. Whether multi-agent wins over single-agent depends on whether the parallelism and specialization benefits outweigh this coordination cost.

It is tempting to assume more agents always helps, but empirical results show a conditional pattern. Wang et al. (2023) found that adding agents beyond a certain threshold can hurt performance on structured reasoning tasks, because coordination noise accumulates faster than individual agent improvement. The sweet spot depends heavily on task decomposability. A task that truly breaks into five independent parallel subtasks benefits from five agents; a task that is fundamentally sequential does not.

Understanding this tradeoff requires thinking carefully about what makes a task decomposable. A subtask is truly independent if it does not share output with another subtask in progress, if its requirements can be specified completely before it starts, and if its result can be validated in isolation. Tasks that appear independent can turn out to be deeply entangled once agents begin executing them. A research task about "government subsidies" and another about "EV adoption rates" seem independent until you discover that the most authoritative source on adoption rates is a government subsidy policy report, creating an implicit dependency. The orchestrator's ability to anticipate and handle such entanglement is a key determinant of multi-agent system quality.

The history of multi-agent systems in AI stretches back decades before LLMs. Classical multi-agent research in the 1990s and early 2000s, exemplified by work on robotic swarms, distributed problem solving, and game-theoretic agent interactions, established foundational concepts that modern LLM-based systems rediscover and extend. The shift from hand-coded rule-based agents to LLM-based agents changes the implementation considerably but does not change the fundamental coordination challenges. Whether agents are robotic components communicating over a network or LLMs calling APIs over HTTP, the same questions arise: how do they share state, how do they divide work, and how do they recover from partial failures?

One aspect of this history worth understanding is why earlier multi-agent systems did not achieve the broad applicability that modern LLM-based systems promise. Classical agents were highly domain-specific: a multi-agent system for scheduling warehouse robots worked within a narrow, well-defined problem space with a fixed set of actions, reliable sensors, and deterministic outcomes. Building such a system required extensive domain engineering for every new application. LLMs change this equation by providing agents with broad general-purpose reasoning and language understanding, allowing the same underlying model to be specialized for dramatically different roles through prompting alone. This is what makes modern multi-agent systems feasible at scale in a way that classical approaches were not.