Claude multi-agent workflows are most useful when a task can be divided into distinct pieces of work—or when those pieces can only be identified as the task unfolds—and the gains in quality or speed outweigh extra coordination, tool use, and latency. Start with the simplest workflow that could work, test it on representative tasks, and add agents only when evaluation shows a meaningful improvement.
What is a multi-agent workflow with Claude?
A multi-agent workflow assigns separate work to multiple Claude instances and coordinates their results. A lead agent might define a strategy, delegate research questions to workers, and combine their findings. The workers may run in parallel or in sequence, and the lead may use tools or code to manage the handoffs.
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That is different from a fixed workflow, where code routes work through predefined steps. An agentic system gives a model more responsibility for deciding what to do next and which tools to use. The distinction matters: if the steps are predictable, ordinary code or a simpler model workflow may be easier to control. Anthropic recommends starting with a simple design and adding agentic complexity only when evaluations show that it improves results (Anthropic’s guide to effective agents).
Which workflow pattern should you choose?
Choose the pattern based on whether subtasks are predictable, independent, or dependent on earlier results—not on how many agents a system can run. The patterns below reflect the structures Anthropic describes in its agent-pattern guide.
| Pattern | How it works | Good fit | Watch for |
|---|---|---|---|
| Predefined parallelization | Code splits work into known, independent tasks and runs them concurrently. | Independent research questions, known components, or separate perspectives where parallel speed or breadth matters. | Tasks that depend on one another, duplicate assignments, or parallel calls whose cost is not justified by the benefit. |
| Orchestrator-workers | A lead model decides what subtasks are needed, delegates them, and synthesizes the results. | Complex requests where the number or nature of subtasks is hard to predict before seeing the input. | Vague assignments, overlapping work, missed coverage, and a lead that spends more effort coordinating than the task warrants. |
| Evaluator-optimizer | One model generates an output; another evaluates it and returns feedback, potentially through repeated rounds. | Work where specific, actionable feedback can improve a draft or solution. | An evaluator that is poorly calibrated or that approves weak work. Model self-review should be tested, not assumed reliable. |
| Sequential workflow | Steps run in a defined order, with later steps using earlier outputs. | Tasks with real dependencies or a required sequence, such as producing an intermediate result before acting on it. | Using a model for predictable steps where deterministic code would be simpler and more consistent. |
For an orchestrator-worker design, the lead determines the task breakdown dynamically; it is not simply a fixed set of parallel calls. That flexibility is useful when the input determines which investigations are relevant, but it also makes the quality of delegation and synthesis central to the result (Anthropic’s account of its multi-agent research system).
When are subagents worth using?
Subagents are a good candidate when a piece of work can be isolated, assigned a clear result, and checked independently. Examples include exploring separate parts of a codebase early in a task or verifying a particular question without filling the main agent’s context with every exploratory detail. Anthropic’s Claude Code best-practices article describes these uses; they are opportunities to preserve context, not a rule to delegate every complex task.
- Consider delegation when subtasks are distinct, can proceed independently, need focused investigation, or benefit from separate verification.
- Prefer a simpler workflow when the steps are known and sequential, the task is small, or workers would need to repeat the same context and reasoning.
- Check the economics by comparing task quality and completion time with added model calls, tool use, tokens, latency, and recovery effort.
Anthropic reported that its Claude Opus 4-led system with Claude Sonnet 4 subagents improved performance by 90.2% over single-agent Claude Opus 4 on Anthropic’s internal research evaluation in 2025. This is a result for that system and that internal evaluation—not a general expected gain or a forecast for another workload (Anthropic’s system description).
How should you delegate work and prevent duplication?
Give each worker a bounded assignment that is distinct from the others. In Anthropic’s description of its research system, vague assignments caused duplicated research and gaps. The practical fix is to specify both what a worker should do and what it should leave to the other workers or lead.
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- Boundaries: Name exclusions, dependencies, and areas owned by other workers.
- Sources and tools: Specify permitted or preferred sources and tools where relevant.
- Output format: Request a concise conclusion with supporting evidence in a consistent structure the lead can compare.
Have the lead track coverage against the original request while it synthesizes results. That makes omissions easier to spot than relying on a polished-sounding final summary. Anthropic discusses assignment clarity, specialization, and synthesis in its multi-agent system account.
For a large artifact—such as a report, code change, or visualization—consider having a worker save the result somewhere durable and return a concise summary plus a reference to it. Relaying an entire artifact through the lead can consume context and lose useful detail; a reference lets the lead inspect what it needs without copying every intermediate result into its conversation.
How do context and tools affect the design?
Each agent has limited context, and coordination itself consumes some of it. Keep worker returns high-signal: conclusions, evidence needed to assess them, unresolved questions, and references to substantial artifacts. Avoid forwarding long intermediate traces or irrelevant tool output to every agent.
Design tools to expose distinct actions and return the information needed for the next decision. For large outputs, filtering, pagination, range selection, and sensible truncation can keep irrelevant data out of context. Anthropic’s tool-writing guidance describes a 25,000-token default limit for Claude Code tool responses in that article. It is a product-specific default, not a universal context limit for Claude or other agent systems.
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Long-running tasks may also need a context reset. A reset can provide a clean working context, but only if the system preserves a useful handoff artifact for what has been done and what remains. Anthropic notes that resets add orchestration complexity, token overhead, and latency; they are not a free way to extend a task indefinitely (Anthropic’s long-running harness article).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you tell whether multiple agents improved the result?
Build representative test cases before expanding the architecture. Compare the simplest viable baseline with the proposed multi-agent version on the same tasks. Measure both what the user receives and what the system spends to produce it.
- Outcome: Successful completion or task-specific quality, using explicit criteria.
- Efficiency: Runtime and latency, model and tool calls, and token consumption.
- Reliability: Tool failures, incomplete coverage, duplicated work, and handoff errors.
- Recovery: How often a person or another agent must intervene to correct or complete the work.
Where feasible, reserve held-out tasks for checking whether an apparent improvement generalizes beyond the examples used to tune prompts and tools. Inspect failures, and repeat the evaluation after meaningful changes to models, prompts, tools, or orchestration. Anthropic’s guide to agent evaluations explains why evaluations help reveal behavioral changes before they affect users.
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Do not treat an evaluator agent as an automatic quality guarantee. Anthropic warns that models can be overconfident when judging their own work. Define evaluation criteria, test the evaluator against those criteria, and tune it when its judgments are unreliable (Anthropic’s harness guidance).
What are the main failure modes and safeguards?
- Duplicated work or gaps: Make ownership and boundaries explicit, standardize worker outputs, and check coverage during synthesis.
- Context pollution: Return concise evidence and conclusions; store large artifacts durably and pass references rather than full intermediate output.
- Coordination outweighs the gain: Track latency, tool calls, token use, and recovery alongside quality so a marginal improvement does not hide disproportionate cost.
- Weak self-review: Use explicit criteria and validate evaluator judgments rather than relying on confidence or fluency.
- Unsafe delegation or prompt injection: Treat instructions passed to workers and content returned by them as trust boundaries. A delegated task can encounter untrusted content, and a lead should not blindly accept a worker’s actions or conclusions.
- Confusing tool surface: Give tools clear, distinct purposes and monitor errors and actual use instead of adding tools indiscriminately.
Anthropic’s Claude Code auto mode describes checks around delegation and returned work, including reviewing the worker’s action history in context. That is one product’s safeguard design, not a general security guarantee for other Claude workflows or custom orchestration (Anthropic’s Claude Code auto mode article).
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