How do you design multi agent systems?

Summary slides

The appeal of multi-agent systems is intuitive and easy to understand: a complex task, broken down into smaller, well-defined pieces each handled by its specialist, naturally mirrors how effective human teams already tackle complex work together, and a single agent trying to hold an entire complex task’s full context and reasoning within one continuous, unbroken process can struggle in ways that deliberate decomposition addresses directly and effectively. But this intuition doesn’t automatically or reliably transfer into good multi-agent system design in practice, because the considerable coordination overhead a human team’s shared understanding, mutual trust, and informal, ongoing communication handles naturally and mostly invisibly has to instead be built explicitly, deliberately, and quite carefully into an AI multi-agent system from scratch, and getting that coordination wrong produces a system that ends up measurably worse than a well-built single agent working alone would ever have been for that same task.

How to decide whether a task benefits from a multi-agent structure at all

Before designing a multi-agent system’s structure, it’s worth asking directly whether the task benefits from decomposition into multiple agents, rather than assuming multi-agent architecture is inherently superior to a well-built single agent. A task concretely benefits from decomposition specifically when it involves distinct kinds of reasoning or distinct bodies of specialized knowledge that don’t combine naturally well within one single, continuous reasoning process — a task requiring deep domain expertise in two different areas, for instance, or a task whose different stages benefit from meaningfully different context that would otherwise need to be held simultaneously by a single agent, diluting its focus on any one part of the task.

A task doesn’t benefit from decomposition when its apparent complexity is really just length or volume rather than diversity of reasoning — a single, well-structured agent working through a long but conceptually uniform task, echoing the orchestrator-worker discussion in the related article on AI native design patterns, often handles that kind of task more reliably and more efficiently than an artificially decomposed multi-agent version would, since the decomposition adds coordination overhead without addressing a limitation the single agent had. Teams that default to multi-agent architecture regardless of whether a task’s structure calls for it tend to build systems that are more complex, more expensive, and not meaningfully more capable than a well-designed single agent would have been for that same task.

How to divide responsibility across agents along distinct lines

Once a task has been established to warrant a multi-agent structure in the first place, the way responsibility gets divided across the individual agents matters considerably for how well the resulting overall system goes on to perform in practical use. A well-designed division assigns each agent a distinct, well-scoped responsibility — a research agent that gathers and synthesizes information, a drafting agent that produces content from that synthesized information, a review agent that checks the draft against criteria — rather than dividing the task along boundaries that don’t correspond to different kinds of work, which tends to produce agents whose responsibilities overlap ambiguously and whose outputs conflict or duplicate each other in ways that require extra coordination to resolve.

This division benefits from being drawn around the same kind of specialization that makes human team division effective — distinct skills, distinct information needs, distinct decision-making criteria — rather than an arbitrary division of a task into equal-sized pieces that don’t correspond to meaningfully different responsibilities. Teams that design this division carefully, testing whether each agent’s role is distinct and well-scoped before building the full system around it, tend to produce considerably more coherent multi-agent systems than teams that divide a task more arbitrarily and discover only after building the full system that two agents’ responsibilities overlap confusingly or that an important piece of the task fell into the gap between two agents’ defined scopes.

How communication and handoff protocols determine whether agents coordinate well

The concrete mechanism by which one agent’s output becomes another agent’s input — echoing the structured-output pattern discussed throughout this knowledge base’s coverage of AI native design patterns — is one of the most consequential design decisions in a multi-agent system, because a handoff that loses important context or that the receiving agent misinterprets undermines the entire system’s coherence regardless of how well each individual agent performs its task in isolation.

Building this well means defining explicit, structured formats for what one agent passes to the next, rather than relying on a free-form natural-language handoff that the receiving agent has to interpret without any guaranteed structure, since a structured handoff is both more reliable to parse correctly and considerably easier to inspect and debug when something in the handoff goes wrong. It also means being deliberate about how much context needs to pass between agents versus how much each agent can reasonably re-derive or retrieve independently, echoing the context-minimization discussion in the related article on designing cost-efficient AI systems — passing an agent’s entire reasoning history to every subsequent agent is often unnecessary and costly, while passing too little context risks a receiving agent making a decision without information it needed to make that decision well.

How to choose between a centralized supervisor structure and a more decentralized coordination structure

Multi-agent systems typically follow one of two broad coordination structures, and choosing between them deliberately, with intent, matters considerably for how the resulting system ends up behaving once it’s built and running in practice. A centralized structure has one single, clearly designated supervisor agent actively directing all the other agents, deciding specifically which agent handles which part of the overall task and then synthesizing all of their individual outputs together into one coherent final result, echoing the orchestrator-worker pattern discussed throughout this knowledge base’s coverage of AI native design patterns applied here specifically to coordinating multiple autonomous agents rather than simpler, more mechanical worker steps. A decentralized structure, by contrast, has agents coordinating more directly and independently among themselves, without any single, clearly identifiable point of central control determining the overall flow of the task from start to finish.

A centralized structure tends to be considerably easier to reason about, debug, and evaluate, since the supervisor provides a single, identifiable point where the overall task’s actual flow and final synthesis can be inspected and understood, and it tends to be the right default choice for most production-oriented multi-agent systems specifically because of this comparative simplicity and inspectability. A decentralized structure can handle certain kinds of emergent, unpredictable coordination more flexibly, but it’s considerably harder to debug when something goes wrong, since there’s no single point where the overall task’s flow gets synthesized and inspected, and it’s correspondingly harder to build the kind of reliable evaluation discussed throughout this knowledge base’s coverage of AI native design patterns around a structure whose actual behavior emerges from many agents’ interactions rather than from one identifiable coordination point.

How debugging a multi-agent system differs from debugging a single agent

Echoing the incident-response discussion in the related article on AI native software, debugging a multi-agent system’s failure is harder than debugging a single agent’s failure, because a problematic final output could trace back to any one of several agents, or to a handoff between two agents, rather than to a single, isolated reasoning process a team can examine directly. This means the observability infrastructure discussed throughout this knowledge base’s coverage of AI native architecture needs extension for multi-agent systems: capturing not just each agent’s individual input and output, but the actual sequence of handoffs between agents and what specifically each agent received and produced at each step, so a team investigating a problem can trace the actual path a failure took through the system rather than only seeing the final, already-degraded output.

Building this extended observability well means treating the full multi-agent trace — every agent’s individual reasoning and every handoff between them — as the actual unit of investigation, rather than treating the system as a single black box whose only visible behavior is its final output. Teams that build this tracing in from the start tend to diagnose multi-agent failures considerably faster than teams that only capture the system’s final output, since the latter leaves a team essentially guessing at which agent or which handoff introduced the problem, without any concrete evidence to narrow down the investigation.

How cost and latency compound differently in multi-agent systems than in single-agent ones

Echoing the sequential-call latency discussion in the related article on designing low-latency AI systems and the cost discussion in the related article on designing cost-efficient AI systems, a multi-agent system’s cost and latency compound directly with the number of agents involved in producing an output, since each agent’s reasoning typically requires its model call, and a task requiring several agents’ sequential involvement accumulates the latency and cost of every one of those individual calls.

This means the decision to add another agent to a multi-agent system’s structure should weigh this compounding cost directly, rather than assuming additional specialization is free simply because it improves the system’s conceptual clarity. A task benefiting from an additional agent’s specialized handling justifies that additional cost and latency; a task where an additional agent’s contribution is marginal relative to what a slightly more capable single agent, or a more focused two-agent structure, could have handled just as well doesn’t justify the compounding cost that additional agent introduces. Teams that weigh this tradeoff explicitly for each agent in their system’s structure tend to build considerably leaner, more cost-efficient multi-agent systems than teams that add agents freely without weighing each addition’s actual compounding cost against its measured contribution to the system’s overall output quality.

How to handle disagreement between agents whose outputs conflict

A recurring challenge specific to multi-agent systems is what happens when two agents’ outputs conflict — a research agent surfaces information that a fact-checking agent flags as questionable, or two agents reasoning over related but distinct parts of a task reach conclusions that don’t fit together coherently. A single agent doesn’t face this problem, since it has only one continuous line of reasoning to reconcile with itself, but a multi-agent system needs an explicit mechanism for resolving this kind of conflict rather than silently picking one agent’s output over another’s without any principled basis for that choice.

Building this well typically means designating a resolution mechanism as part of the system’s overall design — a supervisor agent, echoing the centralized coordination structure discussed earlier in this article, that’s specifically responsible for reconciling conflicting agent outputs rather than merely passing them through unexamined, or an explicit escalation path to human review, echoing the human-in-the-loop checkpoint pattern discussed throughout this knowledge base’s coverage of AI native design patterns, for conflicts a supervisor agent’s reasoning can’t confidently resolve on its own. Systems built without this explicit resolution mechanism tend to handle conflicting agent outputs inconsistently, sometimes silently favoring whichever agent happened to run last or whichever agent’s output happened to be structured in a way the downstream synthesis step processed more easily, rather than resolving the conflict based on any principled assessment of which agent’s output was more reliable for that case.

How shared versus isolated context between agents shapes both coordination quality and cost

A further design decision, distinct from the handoff protocols discussed earlier in this article, is how much context agents share throughout a task versus how much each agent operates with its isolated, independently retrieved context. A fully shared-context design gives every agent visibility into the full accumulated context of everything that’s happened so far in the task, which can improve coordination quality since no agent is ever missing information another agent already has, but it carries cost, echoing the context-minimization discussion in the related article on designing cost-efficient AI systems, since every agent’s call now processes the full accumulated context rather than only what that agent’s task requires.

A more isolated design gives each agent only the context relevant to its defined responsibility, reducing cost and keeping each agent’s reasoning more sharply focused on its own task, but it risks a coordination gap if an agent needed some piece of context it wasn’t given access to under the isolated design. Getting this balance right means examining, for a multi-agent system’s actual task structure, which pieces of context need to be shared across every agent — a task’s overall goal and any hard constraints every agent needs to respect, for instance — versus which pieces are specific to one agent’s responsibility and can reasonably stay isolated to that agent alone, rather than defaulting uniformly to either fully shared or fully isolated context regardless of what a task’s actual coordination needs require.

How to prevent and detect coordination failures like infinite loops between agents

A failure mode specific to multi-agent systems, without a clean analog in single-agent design, is a coordination loop — two or more agents repeatedly handing a task back and forth between each other without ever converging on a final result, each agent’s output triggering another round of handoff rather than task completion. This can happen when a supervisor’s synthesis logic, or a decentralized coordination structure’s emergent behavior, doesn’t have a clear, explicit termination condition, letting agents cycle indefinitely without any built-in mechanism forcing the process toward an actual conclusion.

Preventing this well means building explicit termination logic into a multi-agent system’s design from the start — a maximum number of handoff rounds before the system forces a resolution or escalates to human review, or explicit progress criteria that each round of agent interaction needs to demonstrably satisfy before another round is permitted to begin — rather than assuming agents will naturally converge without this kind of explicit, enforced boundary. Detecting this failure mode in production requires the same kind of multi-agent-observability discussed later in this article, specifically monitoring for unusually long handoff sequences that might indicate a coordination loop rather than legitimate, productive back-and-forth between agents working through a task’s complexity.

How to evaluate a multi-agent system’s overall quality, not just each individual agent’s performance

Echoing the evaluation-and-guardrail pattern discussed throughout this knowledge base’s coverage of AI native design patterns, evaluating a multi-agent system well requires measuring more than just each individual agent’s performance on its own narrow task in isolation — a system where every individual agent performs excellently on its own defined responsibility can still produce a poor overall result if the coordination and synthesis connecting those individually excellent agents doesn’t combine their outputs well. This means a complete evaluation approach needs to measure the system’s end-to-end output quality directly, not merely as an aggregate of each agent’s individually measured performance.

Building this well means maintaining an evaluation set specifically built around the full multi-agent task, evaluated against the system’s actual final output rather than only against each agent’s individual intermediate output, while still capturing enough of the multi-agent trace discussed earlier in this article to diagnose, when the end-to-end evaluation reveals a problem, whether that problem traces back to an agent’s individual reasoning or to the coordination and synthesis connecting otherwise well-performing agents. Teams that evaluate only individual agent performance, without this end-to-end evaluation layer, risk a false sense of confidence in a system whose individually well-performing components don’t combine into a well-performing whole.

How to calibrate autonomy differently across different agents within the same system

Echoing the calibrated-autonomy principle discussed throughout this knowledge base’s coverage of AI native principles, different agents within the same multi-agent system often warrant meaningfully different levels of autonomy, depending on the stakes of what each individual agent is responsible for, rather than applying a single, uniform level of oversight across every agent in the system regardless of what each one does. A research agent gathering and synthesizing publicly available information typically warrants considerably more autonomy than an action-taking agent authorized to make an actual financial transaction or modify a customer’s account, even though both agents exist within the same overall multi-agent system working toward the same broader task.

Building this calibration well means treating each agent’s autonomy level as its explicit design decision, echoing the human-in-the-loop checkpoint pattern discussed throughout this knowledge base’s coverage of AI native design patterns, rather than assuming a single, system-wide oversight policy adequately serves every agent’s actual, individually different stakes. A multi-agent system that applies uniform, minimal oversight across every agent risks under-protecting its highest-stakes agent; one that applies uniform, maximal oversight across every agent risks unnecessarily slowing down its lowest-stakes agents with review overhead their actual, modest stakes never warranted. Teams that calibrate each agent’s autonomy individually, based on that agent’s actual responsibilities and consequences, build systems that are both safer where safety matters most and more efficient where efficiency is safe to prioritize.

How agent specialization should be informed by the same multi-model routing discussed elsewhere in this knowledge base

Echoing the multi-model routing discussed throughout this knowledge base’s coverage of AI native architecture and stack, different agents within a multi-agent system often benefit from being powered by different underlying models, matched to each agent’s task rather than uniformly using the same model across every agent in the system regardless of what each one needs to do well. An agent responsible for a comparatively simple, well-defined task — classifying an input, extracting a structured field — often performs just as reliably on a smaller, faster, less expensive model as it would on a larger one, while an agent responsible for complex, open-ended reasoning benefits from a more capable model’s deeper reasoning capacity in a way a smaller model couldn’t provide as reliably.

This model-per-agent matching compounds directly with the cost and latency discussion covered later in this article, since a multi-agent system that thoughtfully assigns each agent the smallest, least expensive model capable of handling that agent’s responsibility well tends to be meaningfully more cost-efficient than a system defaulting every agent to the same, most capable model available, without any of that cost efficiency requiring a corresponding sacrifice in the system’s overall output quality, provided the matching between each agent’s task and its assigned model was done thoughtfully rather than reflexively assigning the cheapest available model to every agent regardless of whether that agent’s task tolerated the resulting capability reduction.

How to test a multi-agent system’s resilience to a single agent’s failure

Beyond the end-to-end evaluation discussed elsewhere in this article, a production-ready multi-agent system needs explicit testing for what happens when one agent fails or produces a clearly inadequate output, echoing the graceful-degradation pattern discussed throughout this knowledge base’s coverage of AI native design patterns. A multi-agent system where a single agent’s failure cascades into a complete system failure, with no fallback or recovery path, is considerably more fragile than one designed to handle an individual agent’s failure gracefully — retrying that agent’s step, falling back to a simpler approach for that piece of the task, or escalating just that portion of the task to human review while still completing the parts of the task the other, still-functioning agents were able to handle successfully.

Building and testing this resilience deliberately means deliberately simulating individual agent failures during development and evaluation — what happens if the research agent returns nothing useful, what happens if the drafting agent times out — rather than only testing the full multi-agent system’s behavior under conditions where every agent happens to perform correctly. Teams that test this resilience explicitly tend to build multi-agent systems that degrade gracefully under the kind of partial failure that production conditions inevitably introduce sooner or later, while teams that only test the happy path where every agent succeeds tend to discover their system’s fragility to partial failure only once an agent failure in production reveals it directly, in front of users depending on the system to work.

Common mistakes teams make when designing multi-agent systems

The single most common mistake, observed across teams building multi-agent systems at nearly every level of experience, is adopting a multi-agent architecture by default, without ever evaluating whether the task’s actual structure benefits from decomposition at all, discussed at considerable length earlier in this article, building a system that’s meaningfully more complex and more expensive to operate without being meaningfully more capable than a well-designed single agent would have been for that exact same task in the first place.

A second mistake, closely related to the first but rooted in a different design decision, is dividing responsibility across agents along boundaries that don’t correspond to distinct kinds of work at all, discussed in detail above, producing agents whose responsibilities overlap ambiguously with one another, or that leave an important part of the overall task quietly falling into the gap between two agents’ individually defined scopes, where neither agent ever takes ownership of it.

A third mistake, particularly easy to overlook because each individual agent’s output looks perfectly reasonable on its own, is relying on unstructured, free-form natural-language handoffs between agents rather than the explicit, deliberately structured protocols discussed at length above, leaving each receiving agent to interpret an unstructured handoff inconsistently and unreliably from one run to the next, in a way that quietly undermines the entire system’s overall coherence regardless of how well each individual agent performs its task in isolation.

A fourth mistake, often chosen specifically because it sounds more architecturally impressive during initial design discussions, is defaulting to a decentralized coordination structure for its perceived sophistication without ever weighing its considerably greater debugging difficulty against that perceived benefit, discussed at length above, building a system that’s measurably harder to inspect, evaluate, and diagnose than the more inspectable centralized structure a task’s actual real-world requirements would have been served just as well, or better, by all along.

A fifth mistake, often discovered at the worst possible time — in the middle of investigating an urgent production problem — is failing to build multi-agent-observability that traces the full, actual sequence of handoffs a request took, discussed at length above, leaving a team unable to diagnose a reported problem beyond simply confirming the system’s final output looked wrong, without any concrete, actionable way to identify which agent, or which handoff between two agents introduced the issue in the first place.

A sixth mistake, one that tends to creep in gradually rather than arriving all at once through any single obvious decision, is adding agents to a system’s structure one at a time without ever weighing each addition’s compounding cost and latency against its measured contribution to overall output quality, discussed at some length above, building an increasingly expensive, increasingly slow system over time whose actual quality improvement never quite justifies the accumulating cost that each additional agent continues to introduce.

A seventh mistake, easy to miss during initial design because it only becomes visible once two agents disagree in practice, is failing to build an explicit conflict-resolution mechanism for exactly that moment when two agents’ outputs substantively disagree, discussed at length earlier in this article, letting the system handle conflicting outputs inconsistently from run to run, sometimes silently favoring whichever agent simply happened to run last, rather than resolving the disagreement based on any principled assessment of which agent’s output was more reliable for that case.

An eighth mistake, often chosen out of simple convenience rather than any deliberate architectural reasoning, is defaulting uniformly to either fully shared or fully isolated context across every single agent in the system, discussed in detail above, rather than carefully examining a task’s actual coordination needs to determine which particular pieces of context need to be shared across every agent involved, and which pieces can reasonably stay isolated to the one agent that needs them and no other.

A ninth mistake, dangerous specifically because it can silently consume cost and real time without producing any visible error at all, is failing to build explicit, enforced termination logic that reliably prevents agents from cycling indefinitely back and forth without ever converging on an actual, usable result, discussed at considerable length above, risking a coordination loop that quietly burns through cost and real time without ever producing a usable output, entirely absent any built-in mechanism forcing the process toward a definite conclusion.

A tenth mistake, one that tends to produce misleadingly reassuring evaluation dashboards, is evaluating only each individual agent’s isolated performance in isolation without ever directly measuring the system’s actual end-to-end output quality, discussed at length above, risking a false sense of confidence in a system whose individually well-performing agents don’t combine, through their coordination and synthesis, into a well-performing whole once the full task is considered together.

An eleventh mistake, rooted in an understandable but ultimately mistaken desire for organizational simplicity, is applying a single, uniform autonomy level across every agent in a system regardless of each individual agent’s different actual stakes, discussed at length above, either meaningfully under-protecting a system’s single highest-stakes agent, or unnecessarily slowing down its lowest-stakes agents with review overhead their modest, actual stakes never warranted in the first place.

A twelfth mistake, easy to make simply because it requires no additional decision-making at all beyond the initial model choice, is defaulting every single agent in a system to the exact same underlying model regardless of each agent’s task, discussed in detail above, missing the considerable cost efficiency available from carefully matching each agent’s assigned model to what that agent’s responsibility concretely requires, without any corresponding sacrifice in the system’s overall output quality as a result.

A thirteenth and final mistake, closing this list but no less consequential for arriving last, is testing a multi-agent system exclusively under favorable conditions where every single agent happens to perform correctly, discussed at considerable length above, without ever deliberately testing the system’s resilience to one agent’s failure, discovering the system’s actual fragility to partial failure only once an agent failure occurs in production and reveals it directly, in front of users who were depending on the system to work.

What ultimately connects all thirteen of these mistakes, considered carefully together as a coherent whole rather than as thirteen entirely separate, unrelated missteps each arising from its isolated cause, is treating multi-agent architecture as an unconditionally, automatically superior default choice, adopted and then built out without the deliberate design attention each of its distinct considerations — task decomposition, responsibility division, handoff protocols, coordination structure, conflict resolution, context sharing, termination logic, end-to-end evaluation, per-agent autonomy, per-agent model selection, and resilience testing — requires to produce a system that outperforms a well-designed single agent. Teams that address each of these considerations deliberately and thoroughly, rather than simply assuming multi-agent complexity is inherently valuable just because it sounds more architecturally sophisticated, tend to build multi-agent systems that are measurably more capable than a well-built single agent would ever have been for that same task, rather than systems that end up merely more complex, more expensive to operate, and considerably harder to debug without ever becoming meaningfully more capable in return for all that added complexity.