O que são sistemas multiagentes? Um guia prático

Key takeaways

• A multi-agent system uses multiple agents that interact to complete tasks, coordinate decisions, or pursue separate goals.
• Clear responsibilities, controlled access to tools, and reliable handoffs matter more than the number of agents.
• Start with a simple workflow. Add agents only when dividing the work produces a measurable improvement.

A multi-agent system is a collection of software or robotic agents that interact within a shared environment, often working together toward a goal. In modern AI applications, this commonly means several AI agents dividing a workflow into roles such as researcher, analyst, and reviewer.

How it works

An agent is a component that observes information, makes decisions, and takes actions within defined limits. An AI agent might read a request, choose a tool, inspect the result, and decide what to do next.

Multi-agent systems are broader than chatbots or large language models. They can also include robots, rule-based software, and simulated agents. Some cooperate; others compete or negotiate.

For a practical business example, imagine a team preparing a supplier comparison:

1. A coordinator receives the request. It identifies the requirements and assigns work.
2. A research agent gathers information. It uses approved sources and records supporting evidence.
3. An analysis agent compares options. It evaluates the findings against the requirements.
4. A review agent checks the draft. It looks for unsupported claims and missing information.
5. A person approves the result. Any purchasing decision stays behind a human approval step.

These roles do not necessarily need different AI models. The same model can power several agents with different instructions, tools, and permissions.

How agents coordinate

Agents may communicate through messages, shared records, or a central coordinator. Common arrangements include:

• Sequential handoffs: One agent finishes before another begins.
• Parallel work: Several agents tackle independent subtasks at the same time.
• Coordinator-led teams: A central agent delegates tasks and combines results.
• Peer collaboration: Agents exchange information without a single central decision-maker.

The surrounding workflow should define what each agent receives, what it must return, and what happens when it fails.

Not every multi-step AI workflow is a multi-agent system. A fixed sequence of prompts may simply be a pipeline. The distinction is whether multiple components act as agents, making decisions within their assigned responsibilities.

Real-world examples

The following are illustrative designs, not claims about particular product deployments.

Customer support triage

A triage agent identifies the issue, a knowledge agent retrieves relevant policy information, and a response agent drafts an answer. Sensitive cases move to a human instead of triggering an automatic refund or account change.

This separation can make responsibilities clearer, but a simpler retrieval-and-drafting workflow may be enough for straightforward questions.

Software maintenance

One agent investigates a reported bug, another proposes a code change, and a testing agent runs permitted checks. A developer reviews the proposed change before it is merged.

The important safeguard is not the “team” label. It is the combination of restricted execution, useful test results, and human review.

Research and reporting

Several research agents investigate different parts of a question in parallel. A synthesis agent combines their findings, while a reviewer checks that important statements have supporting sources.

This is useful when subtasks are genuinely independent. It is less useful when every agent repeatedly retrieves the same material.

Robot coordination

In a warehouse, multiple robotic agents may coordinate routes and task assignments. Unlike a text-based research workflow, this involves physical constraints, collision avoidance, and safety requirements.

This example highlights that multi-agent systems are an established computing concept, not just a new name for AI chat teams.

Benefits and limits

The main benefit is division of work. Narrow responsibilities can make complex tasks easier to manage, inspect, and improve. Independent tasks can sometimes run in parallel, and specialized agents can receive only the tools they need.

However, more agents do not automatically mean better results.

Costs and delays can increase. Every handoff may introduce additional model calls, tool use, and waiting time. Parallel work helps only when coordination overhead does not outweigh the gain.

Errors can spread. If a researcher supplies incorrect information, later agents may build a polished answer around it. A reviewer using similar assumptions can miss the same mistake.

Coordination can fail. Agents may repeat work, disagree indefinitely, or pass incomplete information. Structured outputs, retry limits, and clear stopping conditions help.

Security becomes more complicated. Agents reading external content can encounter malicious instructions. Treat retrieved material as untrusted data, restrict permissions, and require approval for consequential actions.

Accountability still belongs to people. Multiple agents agreeing is not proof of correctness. Use source checks, tests, and named human owners for important outcomes.

How to get started

1. Choose one bounded task

Pick a workflow with a clear input and an observable result, such as producing a sourced internal briefing. Avoid starting with an autonomous system that can spend money, delete records, or contact customers.

Define success before choosing tools: accuracy, completion time, cost per task, or how much editing a person must do.

2. Establish a simpler baseline

Try a single agent or a fixed workflow first. Save representative examples and record the results.

Add another agent only when you can explain the benefit—for example, independent research or a separate review step.

3. Choose tools that fit your team

Dify offers a visual editor for building LLM applications and agent workflows, including retrieval-augmented generation (RAG). It can be a starting point for workflows that need access to reference material.

Langflow is an open-source visual builder for AI agents and retrieval workflows. Visual building can help teams understand how components connect.

For TypeScript developers, Trigger.dev supports long-running background jobs and AI agent workflows. It is relevant when work needs to run outside a short web request.

These tools support parts of an agent workflow; choosing one does not automatically create a well-designed multi-agent system. Check current capabilities against your coordination requirements.

4. Define handoffs and safeguards

For each agent, specify its task, allowed tools, input format, output format, and completion condition. Set limits on runtime, retries, and spending where supported.

Log tool actions and handoffs, protect sensitive information, and provide a clear route to human review.

5. Compare before expanding

Run the same tasks through your baseline and multi-agent versions. Check correctness, latency, cost, and failure recovery.

Keep the additional complexity only if it improves outcomes.

FAQ

How is a multi-agent system different from a single agent?

A single agent handles decisions within one agent loop, even if it uses several tools. A multi-agent system distributes decision-making across multiple agents that interact or share an environment.

Does every agent need a different AI model?

No. Agents can use the same model with different instructions, context, and permissions. Different models may be useful for particular tasks, but they are not a requirement.

When should you avoid a multi-agent workflow?

Avoid it when a straightforward script, fixed pipeline, or single agent already performs reliably. Multi-agent coordination is harder to justify when tasks are tightly dependent, budgets are limited, or failures are difficult to detect.