Use a Claude multi-agent workflow when a task has substantial work that can be divided into distinct assignments—or when the right assignments only become clear as the task unfolds. For predictable steps, start with a simpler workflow; delegation adds coordination, context, latency, and tool-use costs, so it is worthwhile only when evaluation shows a meaningful improvement.
What a multi-agent workflow means
Anthropic distinguishes a workflow, where code coordinates a predefined path, from an agentic system, where a model dynamically chooses its process and tool use. A multi-agent setup adds multiple model workers, typically with a lead that assigns tasks and combines their results. More agents do not automatically mean better results: the architecture should fit the task, and its value should be measured against a simpler baseline. See Anthropic’s guide to effective agents.
Choose a pattern based on the work
| Pattern | Best fit | Watch for |
|---|---|---|
| Sequential workflow | Later steps depend on earlier results, or the order is known in advance. | Use deterministic code for predictable steps when model flexibility adds no value. |
| Predefined parallelization | Work can be split into known, independent pieces, and faster completion or separate perspectives are useful. | Parallel calls can waste resources if tasks depend on one another or do not benefit from independent work. |
| Orchestrator-workers | The request is complex and the number or nature of useful subtasks is hard to know in advance. A lead determines the assignments, delegates them, then synthesizes the results. | The lead must coordinate work and reconcile uneven, overlapping, or incomplete results. |
| Evaluator-optimizer | A draft can be improved through a generation-and-feedback loop with concrete criteria. | An evaluator model is not automatically reliable; calibrate and test its judgments. |
These patterns and their trade-offs are described in Anthropic’s agent-pattern guidance. Compare candidate designs on task quality, dependency order, context use, latency, tool and model consumption, and error recovery—not on topology alone.
When to use subagents
Delegate when a worker can own a bounded piece of work that the lead can later compare, verify, or combine. A task is a good candidate when it has a clear objective, an independently useful result, and a manageable handoff. For example, a lead investigating a technical question could assign separate workers to examine distinct components or evidence sources, then synthesize their findings.
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Claude Code’s best-practices article recommends subagents for complex early exploration and for verifying specific questions, which can preserve context for the main task. That is a useful option, not a rule to delegate every step: isolated or independently checkable work is more likely to justify the coordination cost. Read Anthropic’s Claude Code best practices.
How to delegate without duplicated or missing work
Vague assignments can cause workers to research the same ground or leave gaps. Anthropic describes this problem in its account of the multi-agent research system. Give each assignment enough detail that workers know what to do, what to return, and where their responsibility ends. A practical task brief should specify:
- Objective: the question or deliverable this worker owns.
- Boundaries: what is in scope, what is out of scope, and any other worker’s area not to repeat.
- Sources and tools: permitted or preferred sources and tools, where relevant.
- Output format: the structure and level of detail the lead needs to compare results, such as concise findings with supporting evidence.
- Handoff: any unresolved questions, assumptions, or limitations the lead should know about.
During synthesis, check whether the combined results cover the original request and whether apparently separate assignments actually overlap. For a large report, codebase artifact, or visualization, have a worker store the full result externally and return a concise summary plus a reference. This keeps the lead from relaying every intermediate detail through its context. These practices are drawn from Anthropic’s description of its multi-agent research system.
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Keep context and tool use under control
Each agent has limited context, so tool responses should surface useful information rather than dump large datasets or irrelevant intermediate results. Anthropic recommends using filtering, pagination, range selection, and sensible truncation for context-heavy outputs. Its writing-tools article says Claude Code restricts tool responses to 25,000 tokens by default; this is a product-specific default, not a universal context limit. See Anthropic’s guidance on writing tools for agents.
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For multi-step tool work, programmatic tool calling can let Claude orchestrate calls through code, process intermediate results outside model context, and return only useful results to the model. This can reduce context load and inference round trips, but the practical effect depends on the task and implementation; measure it in your own workflow. Anthropic explains the approach in its advanced tool-use article.
Long-running work and resets
A reset can provide a clean context for continuing a long task, while compaction addresses the need to carry forward relevant context. A reset depends on a useful handoff artifact and adds orchestration complexity, token overhead, and latency. Anthropic also cautions that agents can judge their own work too favorably; a separate evaluator may help, but its criteria and performance need testing. See Anthropic’s article on harness design for long-running applications.
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Evaluate the system against a simple baseline
Before adding agents, assemble representative tasks and compare the proposed design with the simplest workflow that could plausibly solve them. Measure both outcome and operating cost so that a quality gain is not mistaken for an overall improvement.
- Define the task cases and success criteria. Choose examples that reflect the work the system will actually handle, and specify what counts as a correct or useful result.
- Run a simple baseline. Record results from a prompt or workflow without multi-agent orchestration.
- Run the candidate design. Use the same task cases and success criteria, changing the architecture rather than quietly changing the goal.
- Track quality and costs. Record task-specific quality or successful completion, runtime or latency, tool calls, token consumption, tool failures, and coordination or handoff errors.
- Inspect failures and repeat after meaningful changes. Use held-out tasks where feasible, and rerun evaluations after significant changes to prompts, tools, or models.
Anthropic emphasizes evaluations as a way to make behavioral changes visible before users encounter them. Its guidance is available in Demystifying evals for AI agents.
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What Anthropic’s 90.2% result does—and does not—show
Anthropic reported a 90.2% improvement for its Claude Opus 4-led, Claude Sonnet 4-subagent research system over single-agent Claude Opus 4 on Anthropic’s internal research evaluation in 2025. This is a result for that system and evaluation, not a forecast of gains for other workloads. The cited account does not establish an independently replicated or cross-domain result. Read Anthropic’s explanation of the multi-agent research system and its evaluation.
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Plan for failure modes and trust boundaries
Overlap and coverage gaps
Reduce duplicated or missed work with explicit ownership, boundaries, output requirements, and source guidance. The lead should check coverage when combining results rather than assume every assignment was completed as intended.
Context pollution and coordination overhead
Prefer concise, structured findings and references to large artifacts over forwarding every intermediate result. Track extra calls, latency, token use, and operational complexity alongside task quality so you can tell whether orchestration is earning its cost.
Weak evaluation
Do not treat an agent’s confidence in its own output as proof of correctness. Use task-specific criteria, inspect failures, and test any separate evaluator rather than trusting its judgments by default.
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Delegation and unsafe instructions
Delegated instructions and returned results are trust boundaries: a worker may take actions or return content that the lead needs to review. Anthropic’s Claude Code auto mode describes a product-specific safeguard design that checks delegation and returned work in the context of the subagent’s actions. It is an implementation detail, not a general security guarantee for other multi-agent systems. See Anthropic’s article on Claude Code auto mode.
Unclear tools
Give agents tools with distinct purposes and clear names, and make each tool return focused, relevant information. Adding tools indiscriminately can make the system harder to operate; measure tool errors and whether agents use tools effectively.
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