Multi-agent systems: how they work and when to use them

The first generation of Artificial Intelligence agents has been dominated by a relatively simple idea: assign a goal to an agent, provide it with tools, and allow it to execute certain actions with a degree of autonomy. But when the processes we want to automate become more complex, a different architectural decision arises: if one agent can execute a task, what do we gain by introducing several?

Multi-Agent Systems (MAS) transfer a logic of specialization to software. Instead of concentrating context, tools, permissions, and responsibilities in a single agent, different agents can take care of specific parts of a process and collaborate to achieve a common goal.

Business interest is growing rapidly. Gartner recorded a 1,445% increase in inquiries related to multi-agent systems between the first quarter of 2024 and the second quarter of 2025 and forecasts that by 2027, 70% of Multi-Agent Systems will use highly specialized agents.

However, distributing a process among several agents also introduces coordination, new dependencies, more communications, additional costs, and a larger attack surface. Therefore, the relevant decision for a company is not to determine how many agents it can deploy, but when specialization provides enough value to justify that complexity.

What are multi-agent systems and how do they work

A multi-agent system consists of different AI agents that interact to achieve individual or shared goals. These components may have different information, tools, privileges, and responsibilities, and they do not necessarily have to have been developed as a single application.

Imagine a system responsible for managing IT incidents. A single agent could receive a request, classify it, consult documentation, review internal systems, diagnose the problem, propose a solution, and decide what action to take.

A multi-agent architecture allows for the separation of those responsibilities. One component can interpret and classify the incident; another can work with the technical knowledge base; a third can consult specific systems; while another checks that an action complies with established policies before authorizing it.

This separation allows for independent control of what information each component receives, what tools it can use, what data it can access, and what level of autonomy it has.

Thus, designing a MAS starts to resemble less configuring a chatbot and more designing a small digital organization. Responsibilities, communication mechanisms, permissions, handoffs, and rules for determining what happens when components produce incompatible results must be defined.

This design layer connects directly with the work of an AI Solutions Architect, as integrating models is only part of the problem. It is also necessary to decide how they interact with data, applications, infrastructure, and business controls.

Specialization is the true reason to use multiple agents

Gartner predicts that by 2027, 70% of Multi-Agent Systems will use closely specialized agents, an evolution that can increase accuracy but also raise coordination complexity.

Specialization makes sense because not all components of a workflow need to use the same model or have the same capabilities.

A classification task can be solved with a relatively small and economical model. A decision requiring complex reasoning may need a more capable model. One agent may only have read permissions, while another needs authorization to execute an action on a business system.

There may even be components from different vendors or specifically developed for a domain.

Separating responsibilities allows for designing the system around three particularly important variables: capacity, cost, and risk.

It also facilitates the independent evolution of certain components. If a more efficient model appears for a specific function, the organization can replace that part without necessarily rebuilding the entire system.

This modularity explains much of the interest in MAS within Artificial Intelligence architectures. But modularity and complexity often advance together: the more components involved, the more important it becomes to control their interactions.

Three different ways to organize collaboration

A Multi-Agent System does not necessarily imply a group of agents freely conversing with each other. The form of coordination depends on the problem and how much control the architecture needs to retain.

An orchestrator distributes the work

In a centralized architecture, there is a component responsible for interpreting the overall goal and deciding which agents should participate.

The specialists receive specific tasks and return their results to the orchestrator, who maintains a broader view of the workflow. This facilitates traceability because there is a clearly identifiable point from which to observe execution.

The downside is that a significant part of the system depends on the orchestrator's ability to make good decisions and transfer context correctly.

An agent delegates when it needs another capability

Another possibility is to allow an agent to discover during execution that it needs a capability it does not possess and transfer part of the work to another.

An agent analyzing a contract, for example, might need another component specialized in financial information to check certain data before proceeding.

The architecture gains flexibility, but handoffs become more important. It is necessary to establish what context should be transferred, who retains responsibility for the task, and how the result is subsequently incorporated.

Coordination can be distributed

The most complex scenarios allow different agents to share information, propose solutions, or coordinate actions without all decisions necessarily going through a single controller.

This is where much of the potential of MAS appears, but also some of its most difficult problems. The company needs to understand not only what each agent did but how the interactions between them produced the final result.

Designing these relationships requires decisions in Software Architecture that go far beyond selecting an LLM or writing good prompts.

More agents also mean more places where something can go wrong

Modularity can reduce the load that each individual agent bears, but it does not eliminate errors. It distributes them.

One component may generate incorrect information. Another may interpret it as valid and use it to make a decision. A third may execute an action based on that decision. The final result may seem coherent even though the error originated several interactions earlier.

Gartner identifies among the challenges of Multi-Agent Systems a larger attack surface, additional integration and monitoring needs, difficulties in controlling costs, and reliability issues arising from errors that can accumulate among agents.

Less obvious behaviors may also arise: components that repeat work, contexts that grow unnecessarily, redundant calls to models or tools, and communication loops that consume resources without bringing the system closer to its goal.

This changes the meaning of observability.

It is not enough to know if each service is available. The organization needs to know which agent initiated an action, what information it received, to whom it delegated, what tools it used, how much the different interactions cost, and where a deviation occurred.

In a distributed agent system, the traceability of the entire workflow becomes part of the architecture.

The collaboration gap: a good individual agent can be part of a bad team

There is another reason not to assume that adding agents automatically improves a system.

Microsoft Research has studied what it calls the collaboration gap: the difference between the capability shown by certain models individually and the performance they achieve when they need to collaborate.

The researchers evaluated 32 open and proprietary models in individual, homogeneous, and heterogeneous configurations. The results showed that models with good individual performance could degrade significantly when the outcome depended on collaboration with another agent.

The experiment even found cases where small models capable of correctly solving problems individually practically stopped doing so in certain combinations. It also observed that allowing the most capable agent to start the process before transferring the work could recover a significant portion of the lost performance.

For a company, this finding introduces an important distinction.

Selecting the model with the best benchmark for each function does not guarantee that the combination will produce the best system.

The quality of handoffs, the ability to convey context, the interpretation of responses produced by other agents, and the mechanisms available to detect disagreements also matter.

Therefore, the evaluation of a Multi-Agent System should check both the individual performance of its components and their ability to collaborate when the outcome depends on the work done by others.

Lateam framework: one agent or a multi-agent system?

Before introducing a distributed architecture, it is advisable to check whether the problem truly needs that separation.

The following framework developed by lateam summarizes some criteria that can guide that decision:

Decision framework developed by lateam to evaluate when multi-agent specialization can justify additional complexity. It does not represent an official industry standard.

The table deliberately contains several “depends.” An architecture should not be chosen solely based on the number of steps in a process.

A workflow may involve ten operations and still function correctly with a single well-designed agent. Another may have four actions but require separation because one of them uses particularly sensitive information or needs permissions that should not be granted to the rest of the system.

The appropriate boundary lies in responsibilities, not in counting tasks.

A business case: automating IT incidents without building a “super agent”

Incident management allows for visualizing this difference.

A company could build a single agent with access to tickets, internal documentation, diagnostic tools, infrastructure information, and certain administrative actions. Technically, this may be possible, but it would concentrate a considerable amount of context and permissions in a single component.

A multi-agent architecture could separate these responsibilities.

A triage component interprets the incident and establishes its priority. Another consults technical documentation and previous cases. An agent with strictly controlled permissions obtains infrastructure information. Another proposes a resolution, and a policy layer checks certain conditions before authorizing sensitive actions.

These names describe functions within the architecture, not new professional profiles.

The benefit arises when that separation allows for applying different policies. The component that consults documentation does not need permission to modify infrastructure. The one responsible for executing an action does not necessarily need access to all the client's information.

Moreover, different components can use different models, contexts, and consumption limits.

This type of architecture also explains why the work of an AI Agent Developer may extend beyond building individual agents. When multiple components collaborate, it is necessary to design interaction contracts, states, tools, evaluations, and mechanisms to manage partial failures.

Governance becomes complicated when each agent has different autonomy

A Multi-Agent System introduces a problem that uniform governance models handle poorly: agents do not necessarily represent the same level of risk.

One may be limited to consulting information. Another may access an internal database. A third may generate recommendations, and another may have tools capable of modifying business information.

Applying exactly the same restrictions to all ignores those differences.

Gartner predicts that by 2027, 40% of companies will downgrade or withdraw autonomous agents due to governance gaps discovered after production incidents. Its recommendation is to classify agents according to autonomy and trust boundaries, applying controls proportional to each agent's capacity for action and access scope.

This becomes even more important when companies begin to accumulate agents. Gartner estimates that an average Fortune 500 company could exceed 150,000 agents in use by 2028, compared to fewer than 15 in 2025, while only 13% of organizations currently believe they have adequate governance to manage them.

In a multi-agent architecture, the organization needs to know who can do what and under what conditions.

Governance must encompass identity, available tools, accessible data, permitted actions, consumption limits, activity logs, and situations that require human approval.

Ensuring a MAS is not just about protecting each individual component. It also involves controlling the relationships and trust boundaries that exist between them.

When a Multi-Agent System adds complexity without sufficient value

The enthusiasm around agentic AI can lead to treating multi-agent architecture as a mandatory evolution. It is not.

Gartner already warned in 2025 that more than 40% of agentic AI projects could be canceled before the end of 2027 due to rising costs, lack of business value, or insufficient risk controls. The firm also noted that many cases presented as agentic do not really need such an implementation.

The same criterion should be applied with greater rigor to MAS.

A Multi-Agent System is likely not the best option when:

• a single agent solves the task with sufficient accuracy and reasonable cost;

• the workflow is short, stable, and predictable;

• dividing responsibilities does not introduce an observable improvement;

• the organization still cannot properly evaluate individual agents;

• there is not enough observability to reconstruct interactions;

• the cost and latency of coordination exceed the benefits obtained.

There are also processes where conventional automation combined with AI at specific points may be more robust than transforming the entire workflow into an agentic architecture.

The useful question is not whether we can technically build it with five agents.

The question is what responsibility we need to separate and what we gain by doing so.

If there is no concrete answer, there is probably also no sufficient architectural justification yet.

Before scaling agents, build the system that surrounds them

A company ready to experiment with Multi-Agent Systems needs capabilities that do not depend exclusively on the quality of the models used.

The architecture must be modular so that responsibilities are sufficiently delineated and a component can evolve or be replaced without rebuilding the entire workflow.

A clear policy on identity and permissions is also needed. Each agent should operate under the principle of least privilege and access only the data and tools necessary to perform its function.

Observability must allow for reconstructing interactions, measuring latency and costs, identifying loops, and locating the origin of a particular decision. And the evaluation must extend beyond individual components to test handoffs, partial failures, contradictory information, and unexpected behaviors.

In this context, Machine Learning specialists can work on models and evaluation, while AI and architecture profiles design the environment where those components operate.

As the system scales, the difference between building a good model and building a good platform for agents becomes increasingly important.

The company does not need more agents; it needs the right combination

Multi-Agent Systems offer a way to divide complex processes among specialized components. That separation can allow for using different models, limiting permissions, reducing unnecessary contexts, and assigning responsibilities more precisely than in a monolithic agent.

But distributing intelligence also distributes dependencies.

The collaboration gap observed by Microsoft offers a particularly useful warning: the quality of the parts does not solely determine the quality of the system. Communication, coordination, governance, and evaluation become top-level capabilities.

The trend towards specialization seems clear. Gartner predicts that 80% of the tangible ROI from agentic AI will come in 2028 from specialized agents focused on specific domains, rather than generalist systems.

That does not mean the answer is to multiply agents indiscriminately.

The criterion should be how many responsibilities are truly worth separating.

When clear specializations exist, different permissions, multiple sources of information, and decisions that benefit from distinct capabilities, Multi-Agent Systems can provide a more flexible and controllable architecture.

When those conditions do not exist, a single well-designed agent may still be the best decision.

The sophistication of an AI architecture is not measured by the number of agents it contains, but by how well each component understands its function, its limits, and its relationship with others.