Multi-agent systems sound impressive. Multiple AI agents collaborating on complex problems, dividing work, combining expertise. The reality is that multi-agent architectures add substantial complexity, and that complexity only pays off in specific situations. Knowing when multiple agents genuinely help - and when a single well-designed agent works better - saves you from building elaborate systems that underperform simpler alternatives.

The Complexity Cost

Before discussing when multi-agent makes sense, be clear about what it costs.

Coordination overhead arises because agents must communicate, divide work, and merge results. This takes time and tokens. Simple tasks that one agent handles in seconds might take longer with multiple agents due to coordination costs alone.

More failure modes appear because each agent can fail independently, and the coordination between agents can also fail. A five-agent system has at least five times as many potential failure points as a single agent, plus the interfaces between them.

Higher latency results from the sequential handoffs between agents. Even with parallel execution where possible, the coordination steps add up. Users notice when their request bounces between multiple agents before returning.

Increased cost comes from both the additional LLM calls and the operational complexity. More agents mean more state to manage, more logs to review, more components to monitor.

None of this means multi-agent is bad. It means multi-agent must provide enough benefit to outweigh these costs. The cases where it clearly does share common characteristics.

Clear Signs Multi-Agent Will Help

The strongest signal is genuinely different capabilities required for different parts of the task. If one part needs web search and document analysis while another needs code execution and data processing, those are natural boundaries for separate agents. Each agent can be optimized - better prompts, better tools, potentially different models - for its particular job.

The key word is genuinely. If the same agent with the right tools could handle all parts of the task adequately, the complexity of multiple agents probably is not worth it. Specialization must provide meaningful improvement, not just conceptual tidiness.

Independent parallel work is another strong signal. When a task involves multiple sub-tasks that can execute simultaneously without depending on each other, parallel multi-agent execution can dramatically reduce total time. Researching five topics takes five times as long sequentially but only one time with parallel execution. The parallelism benefit often justifies the coordination cost.

Isolation requirements sometimes demand separate agents. Different parts of a task might need access to different credentials, different data, or different security contexts. Separate agents with separate permissions can enforce these boundaries more reliably than a single agent with complex conditional access.

Composable capabilities across different use cases benefit from dedicated agents. If you build a research agent that multiple other systems use for information gathering, that research agent becomes a reusable component. Multi-agent becomes an architecture choice that promotes modularity and reuse rather than just a technique for individual tasks.

Clear Signs Single Agent Is Better

Tight context coupling means the work benefits from one agent holding all the context. When each step heavily depends on understanding from previous steps, passing that context between agents loses fidelity or requires expensive context transfer. One agent that accumulates understanding throughout the task often produces better results.

Simple sequential tasks that flow naturally from one step to the next rarely benefit from multiple agents. If the work is essentially one thing, then another thing, then another thing - without branching or parallelism - the coordination overhead of multiple agents is pure cost.

Speed-critical interactions where users expect fast responses struggle with multi-agent latency. A customer support agent that needs to answer in seconds cannot afford to consult multiple specialists. The fastest path is usually a single well-designed agent.

Exploratory and evolving tasks where the path is not predetermined often suit single agents. When the agent needs to try things, learn, and adapt within a conversation, the flexibility of one agent reasoning through the problem beats a rigid multi-agent structure.

Simple problems that current models handle well do not need the overhead. A capable LLM can be a good researcher, analyst, and writer for many tasks. Adding specialization complexity is only worthwhile when the generalist approach is inadequate.

The Decision Process

A practical approach is starting with a single agent and adding complexity only when needed.

Step one: Build the simplest agent that could work. One agent with appropriate tools and a clear prompt. Test it on representative tasks. Observe where it succeeds and where it struggles.

Step two: Identify specific limitations. If the agent is slow, is it because sequential tasks could be parallel? If quality is poor, is it because some parts need different capabilities? If it fails on complex tasks, would breaking into phases help?

Step three: Consider whether multi-agent addresses those limitations. Adding agents must solve the identified problem, not just add sophistication. If the problem is prompt quality or tool selection, fix those before adding agents.

Step four: If multi-agent is warranted, design the minimal structure that addresses the limitation. Do not create agents for every conceivable sub-task. Create agents where specialization or parallelism provides clear benefit.

Step five: Measure the improvement. Does the multi-agent version actually perform better on the metrics that matter? If the complexity added does not deliver measurable improvement, simplify back to the single agent.

This iterative approach avoids both the trap of over-engineering with unnecessary agents and the trap of under-engineering tasks that genuinely benefit from collaboration.

A Framework for Evaluation

When evaluating whether multi-agent makes sense for a specific use case, consider these dimensions:

Tasks that lean mostly toward the single agent column probably do not need multi-agent. Tasks that lean toward the multi-agent column are good candidates. Mixed results require judgment about which factors dominate.

Common Pitfalls

Agent proliferation is the most common mistake. Teams create many agents because it feels thorough, not because each provides distinct value. Resist the urge to add agents for organizational tidiness. Every agent must earn its place through genuine contribution.

Insufficient specialization undermines multi-agent value. If your specialists are basically the same agent with different names, you have coordination cost without specialization benefit. Specialists should differ meaningfully in tools, prompts, or capabilities.

Over-centralized orchestration creates bottlenecks. If the orchestrator does too much work itself, it becomes the constraint. Orchestrators should coordinate, not execute. Push execution to specialists.

Rigid structures for flexible problems cause brittleness. If you design a fixed multi-agent pipeline but the actual task varies unpredictably, the structure fights the work. Reserve multi-agent for tasks with predictable structure.

Measuring the wrong thing leads to false confidence. Multi-agent systems produce more activity - more agents doing more things. Activity is not value. Measure outcomes: task completion quality, total time, user satisfaction. Complexity metrics miss the point.

Hybrid Approaches

Not every choice is binary. Some designs use a single primary agent with the option to delegate specific sub-tasks when warranted.

An agent might handle most requests alone but spawn a specialist for particularly complex research tasks. The default is simple; complexity appears only when needed. This gets many benefits of multi-agent without the constant overhead.

The underlying capability is agents that can call other agents as tools. Whether to invoke that capability is a design choice, not a fixed architecture. The most flexible systems provide the option and let the task determine usage.

For teams building agent systems, inference.sh supports both single-agent and multi-agent architectures with the same underlying infrastructure. Sub-agents are tools that orchestrators can call when delegation makes sense. You choose the architecture that fits your task without changing platforms.

The right question is not whether multi-agent is good or bad. It is whether multi-agent is right for this particular task. Understanding the trade-offs and evaluating honestly leads to better architecture choices than defaulting to either extreme.

FAQ

How do I know if my multi-agent system has too many agents?

Look for agents that rarely get used, agents that always execute in the same sequence like a rigid pipeline, or agents whose outputs are trivially simple. If removing an agent would not noticeably degrade results, it probably should not exist. Another signal is excessive coordination time - if your agents spend more time handing off than working, you have more agents than the task needs. Start by removing the agents you are least confident about and measure whether quality suffers. Often, fewer well-designed agents outperform many mediocre ones.

Should I use different models for different specialist agents?

Using different models can provide genuine benefit when specialists have different requirements. A research agent that needs broad knowledge might use a different model than an analysis agent that needs strong reasoning. A fast summary agent might use a smaller model than a thorough research agent. However, model mixing adds operational complexity - different APIs, different capabilities, different failure modes. Start with the same model everywhere and change models only where you have evidence a different model performs meaningfully better for that specialist's specific task. Do not mix models just for variety.

How do I handle a multi-agent task where one specialist is much slower than others?

Slow specialists create bottlenecks that can negate parallelism benefits elsewhere. First, understand why the specialist is slow - is it the model, the tools, or the task? If it is the task, consider whether it can be broken into smaller parallel pieces. If it is the tools, can they be optimized or replaced? If the slowness is fundamental, design around it - perhaps that specialist handles a broader scope so it is called less frequently, or perhaps the orchestrator can do useful work while waiting. Caching results from slow specialists across similar requests can also help if the same inputs recur.