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Use the smallest architecture that can meet your quality, speed and governance targets. Choose deterministic code or a direct model call for fixed, predictable work; choose one agent when a single context can plan and use several tools; add subagents only when a capability needs its own reasoning loop, context, specialist tools, permissions or independent failure handling. Split further only when parallelism or a hard boundary produces more value than the extra handoffs, latency, tokens and maintenance.
The practical test is incremental: measure a simple implementation against explicit success criteria, then promote a step to a subagent only when the evidence shows that isolation or specialization solves a real problem.
What is the difference between a tool, one agent and a subagent?
They are three levels of execution autonomy. A tool performs a defined operation; an agent plans and acts across a workflow; a subagent is an agent given a bounded responsibility that can reason, use tools and return an artifact to a parent or coordinator.
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|---|---|---|---|
| Deterministic code | Fixed, repeatable transformations | Same inputs and rules produce predictable outputs | Cannot resolve ambiguous instructions without added logic |
| Direct model call or tool | One quick, stateless operation | Receives parameters, completes one step and returns a result | Limited ability to plan, recover or interpret changing context |
| One agent | Multi-step work in one domain and context | Selects tools, sequences actions, checks progress and synthesizes an answer | Context can become crowded; one failure may affect the whole run |
| Subagent | A capability needing its own reasoning loop or boundary | Works from a defined objective and contract, then reports back | Extra calls, handoffs, state management, latency and monitoring |
A subagent is not automatically a better prompt. It is a separate execution unit with a reason to be separate. AWS guidance identifies independent reasoning, separate context or memory, multi-step tool orchestration, a different model or prompt, and independent failure isolation as signals that a capability should be promoted from a tool to a subagent.
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Can code or a direct model call complete the task?
Start with the least autonomous method. If the step is deterministic, a function, query, parser or ordinary workflow is easier to test, debug and operate than an agent.
Use code for stable rules
Examples include validating a schema, converting file formats, calculating a total, applying a known routing table or extracting a value with a reliable parser. Keep these operations as code even when an agent is present; the agent can decide when to call the function, but it should not recreate the function in prose.
Use a direct model call for one bounded interpretation
A single call is appropriate when the model receives all required context, performs one transformation and does not need to decide which tools to call next. Classification, rewriting to a specified format or producing a short summary can fit this level when the output can be checked immediately.
Azure describes direct model calls as the lowest-complexity option. Treat that as a design default, not a limitation: adding an agent layer creates more places for malformed arguments, retries and partial results unless it solves a demonstrated problem.
When is one agent enough?
Use one agent when the workflow is multi-step but shares one domain, one working context and one set of permissions. The agent can query data, call APIs, inspect results and synthesize an answer without handing work to another autonomous process.
Signs that a single agent fits
- The steps are sequential and each step depends on the previous result.
- The same instructions, memory and tools are needed throughout.
- The task is narrow enough that context remains understandable and within the model’s limit.
- Speed and operating cost matter more than parallel execution.
- A single set of permissions and one approval path is sufficient.
- Prompt, tool and success-criteria improvements can meet the quality target.
Google recommends beginning with one agent while refining prompts, tools and success criteria. Microsoft likewise advises running a single-agent test for most use cases unless architectural separation is mandatory. A sequential workflow is not, by itself, a reason to create subagents.
When does a capability deserve its own subagent?
Create a subagent when the work has a meaningful boundary that can be expressed as an input, output and permission contract. The boundary should improve reasoning quality, reduce context pollution, isolate risk or enable useful parallelism.
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A worker may need to plan several searches, inspect intermediate results, retry failed calls and decide when its own objective is complete. If forcing that loop into the parent makes the parent prompt unwieldy or causes it to lose track of its primary objective, isolate it.
Separate context or memory
Large documents, noisy logs or a repository-wide investigation can crowd out the coordinator’s instructions. A subagent can receive only the relevant slice, produce a bounded artifact and return a concise report. This is context isolation, not merely a way to add more model calls.
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Specialist model, prompt or tools
Use a separate worker when the capability needs a different domain vocabulary, model, system prompt or tool set. A security reviewer, data analyst and copy editor may require different instructions and evidence standards even when they contribute to one deliverable.
Independent failure isolation
If one operation can time out, return an uncertain result or require a fallback without invalidating the rest of the workflow, a subagent boundary lets the coordinator continue or request a retry. Define what happens when the worker fails; separation without a fallback only relocates the failure.
Permission or governance boundary
Separate agents when credentials, data domains, compliance rules or organizational ownership differ. Limit each worker to the minimum tools and data it needs, and make the parent consume a controlled artifact rather than receiving unrestricted access.
Parallel, reusable work
Independent analyses can run concurrently: examining separate repositories, researching distinct sources or evaluating different scenarios. Parallelism helps only when the subtasks are genuinely independent and the coordinator can combine their outputs without serial rework.
How should I score a task before choosing an architecture?
Break the workflow into concrete steps, then score each step before deciding where an agent boundary belongs. A lightweight assessment exposes whether the proposed split addresses a real constraint.
- List the steps. Write the input, action, tools and expected output for every stage.
- Rate repeatability. Mark the step as deterministic, mostly repeatable or highly variable. Deterministic steps normally belong in code.
- Rate impact. Identify what happens if the step is wrong: a cosmetic defect, a delayed answer, a financial loss or a security incident.
- Rate error detectability. Decide whether an automated check, schema, test or reviewer can reliably catch an error.
- Rate time sensitivity. Record the acceptable end-to-end latency and whether concurrent execution changes the result.
- Mark boundaries. Note changes in context, domain, model, tools, permissions, owner or fallback behavior.
- Estimate coordination cost. Count additional model calls, handoffs, state writes, retries, logs and human approvals.
- Choose the smallest design. Keep the step in the parent unless a boundary, specialization or parallelism creates a measurable benefit.
Delegation does not transfer accountability. Microsoft states that the human remains responsible for reviewing, validating and approving the result, even when work is delegated to an AI system.
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What boundaries justify splitting an agent?
Use an interface that makes the split observable and enforceable. A useful boundary has a clear objective, bounded inputs, a structured output and explicit completion checks.
Context boundary
Pass only the documents, records or excerpts required for the worker’s objective. Require citations, line references or evidence fields when the parent must verify the result. Return an artifact identifier for large outputs instead of copying the entire payload into the coordinator’s prompt.
Tool and permission boundary
Declare the worker’s allowed tools, data sources and write operations. Read-only analysis, for example, should not inherit the parent’s ability to modify production records. Log every tool call under the worker identity so an auditor can reconstruct the decision.
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Define acceptance tests before execution: required fields, valid ranges, evidence coverage, policy checks or a minimum confidence threshold. A coordinator should be able to reject, retry or route an incomplete artifact without guessing what “done” means.
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Set a per-agent timeout, retry limit and fallback. A slow worker should return a timeout status or partial artifact rather than block the entire workflow indefinitely. Specify whether the parent can continue with a degraded result, ask another worker or escalate to a human.
Which multi-agent architecture pattern should I use?
Choose the pattern that matches the dependency structure; do not select a pattern because it is fashionable.
| Pattern | Use it when | Key design question |
|---|---|---|
| Supervisor-worker | A coordinator can decompose work and perform centralized quality control | What contract lets the supervisor compare and merge worker artifacts? |
| Pipeline | Stages are naturally sequential and each output feeds the next | What schema prevents one stage’s ambiguity from propagating? |
| Handoff or router | The right specialist is unknown until the request is interpreted | What evidence triggers routing, and where does ownership remain visible? |
| Parallel workers or team | Independent, long-running subtasks can run concurrently | How will conflicts, duplicate work and late results be reconciled? |
| Hierarchical decomposition | Open-ended research or planning must be progressively broken into smaller tasks | Can the quality gain justify more model calls, latency, cost and maintenance? |
Supervisor-worker designs provide centralized control but can bottleneck on the coordinator. Pipelines are easy to reason about when schemas are stable, while routers are useful when specialization emerges during processing. Hierarchical systems handle ambiguity but have the highest risk of runaway scope.
How do tool calls and subagents differ in practice?
Use a tool for a deterministic, stateless, single-step operation that completes quickly. Use a subagent when the capability must interpret ambiguity, plan several calls, maintain its own context, use a different model or prompt, or fail independently.
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| Question | Tool | Subagent |
|---|---|---|
| Does it need to interpret ambiguity? | No; parameters define the operation | Yes; it can clarify, plan or choose among actions |
| How many actions? | Usually one | Potentially several with intermediate checks |
| Context | Stateless or supplied per call | Own working context or memory may be required |
| Model or prompt | Usually inherited | Can use a specialist model or prompt |
| Failure behavior | Parent handles errors | Can retry, degrade or report failure independently |
| Promotion rule | Default starting point | Promote only after repeated use shows an independent reasoning loop is needed |
A practical promotion test is to instrument the tool. If callers repeatedly add branching logic, multiple retries, context assembly and ad hoc interpretation around it, the capability may have outgrown a tool. If those additions are rare and predictable, keep the tool and improve its interface instead.
When does multi-agent orchestration pay off?
It pays off when quality, elapsed time or governance improves enough to offset coordination overhead. Measure the complete workflow, not just the worker’s isolated output.
Token and compute cost
Anthropic reported on January 23, 2026 that, in its testing, multi-agent implementations typically used 3–10 times more tokens than single-agent approaches for equivalent tasks. Treat this as a reported range under that testing, not a universal multiplier. Budget for coordinator prompts, duplicated context, worker outputs, retries and tool-result serialization.
Latency
Parallel workers can reduce elapsed time when tasks are independent, but orchestration adds startup, handoff and merge time. A serial chain often becomes slower than one agent unless each boundary provides a substantial quality or governance benefit.
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Quality and error detection
Compare answer accuracy, completeness, policy violations and recoverability against a single-agent baseline. Include adversarial and ambiguous cases; a design that wins on easy examples but fails silently on edge cases is not an improvement.
Operational complexity
Count prompts, schemas, queues, state stores, dashboards, on-call procedures and versioned contracts. Microsoft warns that separate agents add execution time and maintenance complexity. If the team cannot observe and debug the handoffs, the architecture is not ready for production.
How should I prototype and evaluate a subagent design?
Expand in controlled stages so every added boundary has evidence behind it.
- Define the baseline. Implement the deterministic, direct-call or single-agent version first. Record quality, latency, token use, tool errors and reviewer effort.
- Write the contract. Specify the worker’s objective, inputs, outputs, permissions, completion checks, timeout and fallback.
- Build one boundary. Extract the highest-value specialist or the noisiest context, not several workers at once.
- Use an artifact store for large results. Return a stable identifier, metadata and a concise summary; keep full documents outside the coordinator prompt.
- Run representative evaluations. Include normal requests, ambiguous instructions, missing data, tool failures, prompt-injection attempts and concurrent runs.
- Compare end-to-end metrics. Track quality, latency, tokens, cost, failure rate, escalation rate and maintenance effort against the baseline.
- Set a stop rule. Keep the simpler design when gains do not exceed the measured overhead. Add another worker only after the first boundary remains reliable.
The Canadian Centre for Cyber Security recommends threat-model-based scenarios, edge cases beyond normal training conditions, evaluation across autonomy levels, changes in tool and resource access, and variation in context, timing and the presence of other agents. It recommends continuous capability evaluations throughout the development lifecycle.
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What governance controls should every subagent system have?
Governance is part of the architecture, not a final review step.
- Named objective and owner: identify who approves the worker’s purpose and who reviews high-impact outputs.
- Least-privilege access: grant only the credentials, data and write operations required by the contract.
- Traceability: record prompts, model versions, tool calls, inputs, outputs, approvals and handoffs with timestamps.
- Human checkpoints: require approval before irreversible, regulated, financial or externally visible actions.
- Isolation: prevent untrusted documents or worker output from silently changing system instructions or permissions.
- Timeouts and circuit breakers: stop loops, cap retries and limit total spend per request.
- Fallback behavior: define a safe degraded response and an escalation route when evidence is missing or workers disagree.
- Contract versioning: reject incompatible artifacts rather than allowing schema drift to create silent errors.
What mistakes make teams add subagents too early?
Most failed designs split for appearance rather than necessity. Correct the underlying problem before adding another autonomous process.
Using agents for deterministic work
Replace prose instructions with a function, validator or query when the rule is stable. This improves repeatability and reduces both token use and attack surface.
Splitting a sequential task with shared context
If every worker needs the same full conversation and must wait for the previous worker, handoffs add latency without creating a useful boundary. Improve the single agent’s context management first.
Creating specialists without distinct expertise
Two agents with identical prompts, tools and permissions usually duplicate work. Specialization should be visible in the model, instructions, data or authority each worker receives.
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Returning unbounded prose
Require structured artifacts with status, evidence, assumptions and next actions. Unbounded reports make merging and validation harder and encourage the coordinator to trust unsupported conclusions.
Ignoring disagreement and partial failure
Parallel workers can produce conflicting answers, and a timed-out worker may leave an incomplete set. Define quorum, tie-breaking, confidence and escalation rules before enabling concurrency.
Measuring only local quality
A worker can score well while the overall system gets worse because the coordinator misroutes requests, duplicates context or cannot verify the artifact. Evaluate the user-visible result and the operating cost.
What is the practical decision tree?
Apply these questions in order for each workflow step:
- Can code or a direct model call complete it deterministically? If yes, use that method.
- Does the workflow need several tool calls or a plan within one domain and context? If yes, use one agent.
- Is there a real boundary in reasoning, context, permissions, domain expertise or execution? If no, keep the work in the parent.
- Can the subtask run independently or benefit from specialization or parallelism? If no, keep it in the parent.
- Do expected quality, speed or governance gains exceed handoff latency, token cost and operational complexity? If no, do not split.
- Can you prototype and measure the design against explicit criteria? If no, improve observability and evaluation before expanding.
The answer is therefore usually “no subagents” for a small, predictable task; “one agent” for a bounded multi-step workflow; and “subagents” only for demonstrated boundaries. Revisit the choice when requirements, tools, data sensitivity or latency targets change.
Frequently Asked Questions
How many subagents should I start with?
Start with one extracted capability, not a full team. Choose the step with the clearest contract and highest expected benefit, then add another only after end-to-end measurements show the first boundary is reliable and worthwhile.
Do subagents always run in parallel?
No. A subagent can be called serially when it owns a distinct reasoning loop or permission boundary. Parallel execution is useful only when the subtasks are independent and their outputs can be merged safely.
What should a subagent return to its parent?
Return a structured artifact containing status, the result, evidence or references, assumptions, confidence and recommended next action. Include an explicit failure or partial-completion state so the parent does not mistake silence for success.
How do I keep one agent from becoming too large?
Measure context size, tool-selection errors and recovery behavior rather than splitting by arbitrary prompt length. Isolate the noisiest or most specialized capability, pass only the necessary context and keep the remaining workflow in the parent.
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