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A multi-agent system divides an AI workflow among coordinated roles—for example, a planner that assigns work, executors that complete bounded tasks, and a reviewer that checks the result. It is useful when the work genuinely benefits from parallelism, specialization, or an independent check. It is not automatically better than one agent: extra agents also add handoffs, latency, cost, and failure points. Choose the workflow topology to fit the task, then test it against observable outcomes.
When should you use a multi-agent system instead of one agent?
Start with the task, not the number of agents. A single agent with tools is often the clearest starting point for bounded work, especially while you are still refining prompts, tools, and core logic. A multi-agent design becomes more compelling when a task has distinct responsibilities, genuinely independent subtasks that can run concurrently, or a need to check a result through a separate review stage.
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The key architectural question is whether the work can be divided without losing important context or creating more coordination than the division saves. A task that depends on a precise sequence of decisions may be slowed or weakened by delegating between agents. A task that contains independent lines of investigation may benefit from parallel workers, provided someone can reconcile their results.
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- Split the work when responsibilities are meaningfully different, independent work can run in parallel, or a separate evaluator can apply clear acceptance criteria.
- Do not split merely to add role names. Roles can be separate agents, model calls, or logical stages; they do not require three different models or even three separate agents.
Google Cloud recommends starting with a single agent while developing the core logic, prompts, and tools, then considering delegation for distinct responsibilities. OpenAI’s practical guide likewise recommends adding capabilities incrementally and keeping complexity manageable. The practical baseline is therefore not “use as many agents as possible,” but “use the simplest workflow that reliably meets the success criteria.”
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What is the difference between a planner, an executor, and a reviewer?
These names describe responsibilities, not universal product roles. One model can perform more than one responsibility in different stages, or a system can assign them to separate agents. The useful distinction is what each stage is accountable for producing.
Planner, lead, or manager
The planner interprets the goal, breaks it into subtasks, decides their order or delegation strategy, and may synthesize the returned work. In a centralized manager pattern, this lead retains control over the workflow. Its output should make the next actions clear: for example, a task list with boundaries, dependencies, and required deliverables.
Executor, worker, or specialist
An executor performs an assigned task using a defined responsibility, suitable context, and relevant tools. A worker should return a usable artifact or finding—not just an unstructured transcript. The lead needs enough information to integrate the work, understand its basis, and identify missing or conflicting results.
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A reviewer compares a result with explicit criteria. It can approve the result, reject it, or give actionable feedback for another attempt. A reviewer is not automatically a source of truth: fluent critique is not proof that a claim is correct. Ground checks in tests, authoritative data, constraints, or the environment whenever possible.
Which workflow topology fits the task?
Topology is the rule for how work moves between stages and agents. Choose it according to dependencies: whether subtasks are independent, must happen in a known order, need centralized coordination, or can be reviewed and revised.
| Pattern | Work flow | Good fit | Main trade-off |
|---|---|---|---|
| Single agent with tools | One agent plans and acts over multiple steps. | Bounded tasks, early development, or workflows where responsibilities are not sufficiently distinct to justify delegation. | A large tool set or sharply different responsibilities may make the agent less effective. |
| Sequential pipeline | Fixed stages pass outputs forward in a known order. | Structured, repeatable processes whose stage order is predictable. | Less flexible when conditions change or a stage should be skipped. |
| Parallel workers | Independent subtasks run concurrently; a later stage synthesizes their results. | Separate facts, perspectives, or analyses that can be gathered without waiting on one another. | Uses more resources and creates a synthesis burden; parallelize only genuinely independent work. |
| Centralized manager / orchestrator-worker | A lead assigns work, retains workflow control, and integrates specialist outputs. | A workflow needs a central component to coordinate subtasks and assemble the result. | The manager and inter-agent communication add calls and coordination overhead. |
| Decentralized handoffs / peers | Agents route work to another agent according to specialty. | Workflow ownership can move naturally among specialized agents. | Global context and control are harder to retain; use when handoffs fit the task. |
| Review / critique loop | A generator produces a result; a critic evaluates it; the generator revises when needed. | The result can be judged against explicit criteria and feedback can guide a correction. | Critique and revision rounds add latency and operating cost, so the loop needs a stopping rule. |
Hybrid systems combine these patterns—for instance, a manager can delegate independent research in parallel, then pass the synthesis through a review stage. A hybrid is justified when each added transition solves a real dependency or quality problem; otherwise, it can make the workflow harder to reason about without improving the result.
How do you build a planner–executor workflow?
Make delegation contracts explicit. The planner needs to know what it may delegate, and each executor needs enough context to complete a bounded task and return a result the planner can use.
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- Define success before assigning roles. Specify the task, input conditions, constraints, and observable outcome that counts as success. Avoid a goal such as “do good research” without saying what the final result must contain or how it will be checked.
- Map dependencies. Classify each subtask as independent, sequential, or interdependent. Run independent tasks concurrently; preserve the required order where later work depends on earlier results.
- Write the delegation contract. For each executor, state its responsibility, relevant context, available tools, constraints, and required output. Make outputs concrete—for example, a finding with its supporting evidence, an artifact, or a structured result.
- Define integration behavior. Tell the planner how to handle missing, contradictory, or unusable outputs: retry, ask for clarification, resolve against a source of truth, or escalate. Do not leave conflict resolution implicit.
- Ground actions in the environment. Where tools or an external system are involved, use their results to determine progress rather than relying on an agent’s assertion that an action succeeded.
- Test the whole workflow. Compare its end-to-end outcome with a simpler baseline and inspect whether delegation improves the measured task, not just whether individual agents produce plausible messages.
The planner should remain responsible for the complete task even when execution is delegated. A worker’s locally successful output does not necessarily satisfy the overall goal, and the planner must be able to identify what remains unresolved.
How do you make a review loop useful and bounded?
A generator–critic loop is effective only when “good enough” can be defined and the critic can identify defects the generator is able to fix. A vague instruction to “improve this” often produces another fluent answer rather than measurable progress.
Specify checks and feedback
Separate the checks that matter to the task. Depending on the workflow, these may include factual correctness, task completion, format or policy compliance, and safety. Give the reviewer concrete criteria and ask it to point to the specific unmet requirement or defect, along with an actionable correction. Where possible, pair model review with external validation such as tests, constraints, authoritative data, or a verified environment state.
Set a stopping condition and fallback
Define when the loop ends before it runs: approval against the criteria, a measured quality threshold, a maximum number of iterations, or another explicit state. Also define what happens if the result still fails when the limit is reached, such as returning a failure state or escalating to a person. Google Cloud warns that a wrongly specified termination condition can create an endless loop; each additional critique and revision also consumes time and resources.
Distinguish a claim from an outcome
For tool-using workflows, check what actually happened in the environment. Anthropic’s evaluation guidance distinguishes an agent’s stated success from the final environment state—for example, claiming a reservation succeeded is different from confirming that the reservation exists in a database. A review score should not substitute for that ground truth.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do multi-agent systems improve agent performance?
Sometimes, for some tasks and implementations. The strongest general conclusion is conditional: coordination can help when work is parallelizable and can hurt when work is sequential or tightly dependent.
In a Google Research article dated January 28, 2026, the researchers report a controlled evaluation of 180 agent configurations spanning five architectures—single-agent, independent, centralized, decentralized, and hybrid—across four benchmarks and three model families: OpenAI GPT, Google Gemini, and Anthropic Claude. The reported results illustrate why architecture choice must follow task structure:
- On the Finance-Agent benchmark, centralized coordination improved performance by 80.9% over the single-agent baseline in the study’s tested setting.
- On the sequential PlanCraft benchmark, multi-agent variants degraded performance by 39–70% in the study’s tested settings.
- The study’s predictive model identified the optimal coordination strategy for 87% of unseen task configurations; its reported R² was 0.513.
These are results from that evaluation, not forecasts for a new production workflow. They depend on task characteristics, benchmark, model, topology, and implementation. The predictive model’s reported accuracy also does not mean topology can be selected confidently without testing a workflow in its intended setting.
Anthropic has separately described a company-specific research system using Claude Opus 4 as a lead and Claude Sonnet 4 subagents. Anthropic reports that this system outperformed single-agent Claude Opus 4 by 90.2% on its internal research evaluation. That is a vendor-reported result for its research system and internal evaluation, not an independent comparison across tasks or a general expected gain.
In addition to performance, account for the operational costs of additional agents: more calls and resource use, longer end-to-end latency, more coordination and integration failure modes, a larger evaluation surface, and security or access-control questions. Give each agent only the context and tools its task requires.
How do you evaluate an agent workflow?
Evaluate a run as an interaction with an environment, not just as a final answer. A plausible final response can conceal failed tool calls, incomplete work, or a state change that never occurred. Evaluation should connect the intended task to observable evidence of completion.
- Define task cases and success criteria. Include the input conditions and observable outcomes you expect, including constraints that matter for correctness or safety.
- Keep traces. Record inputs, model outputs, tool calls, intermediate results, and environment changes so failures can be traced to planning, execution, integration, or review.
- Use graders at the right level. Check individual behaviors as well as the end-to-end outcome. A good subtask result does not prove the entire workflow succeeded.
- Run repeated trials when model variation matters. Compare results across runs rather than treating one successful execution as proof of reliability.
- Compare with a simpler baseline. Measure whether the multi-agent workflow improves the outcome enough to justify its added latency, cost, coordination, and maintenance.
- Check the actual environment state. Verify external effects—such as a saved record, completed transaction, or test result—rather than trusting the agent’s final claim.
For a review loop, include the criteria used to approve or reject, the feedback produced, the number of revision rounds, and the final state. This makes it possible to see whether review catches meaningful defects, causes unnecessary revisions, or fails to stop when it should.
Quick Recap
Implementation checklist
- Write down task success criteria and the final observable outcome.
- Classify dependencies before deciding what can run in parallel.
- Use explicit agent responsibilities, appropriate context, and narrowly relevant tools.
- Specify executor deliverables and the planner’s policy for missing or conflicting outputs.
- For a critique loop, define acceptance checks, actionable feedback, a maximum iteration count, and a fallback or escalation path.
- Capture traces and environment outcomes, and test repeated trials where run-to-run variation matters.
- Compare the complete workflow with a single-agent baseline, accounting for latency, resource cost, coordination reliability, evaluation effort, and security controls.
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