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Most tasks do not need a team of AI agents. Start with deterministic code or a direct model call; use one agent when a single context can plan and complete several steps; add subagents only when an independently reasoned, specialized, isolated, or parallel workstream delivers more value than its coordination cost.
That threshold matters because every extra agent adds handoffs, state management, latency, token use, permissions to govern, and another place to fail. Anthropic reported on January 23, 2026, that multi-agent implementations in its testing typically used 3–10 times more tokens than single-agent approaches for equivalent tasks. The right architecture is therefore the smallest one that reliably meets your quality, speed, cost, and governance requirements.
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A tool performs a defined operation, such as running a database query, calling an API, converting a file, or executing a deterministic function. A main agent interprets a request, chooses tools, keeps the working context, and coordinates a multi-step outcome. A subagent is a separate agent with its own reasoning loop, instructions, context or memory, tools, permissions, and completion criteria. It returns an artifact or result to the parent workflow.
The distinction is architectural rather than cosmetic. A function that accepts parameters and returns a result is still a tool even if a language model selected those parameters. A worker that must interpret ambiguity, plan several calls, maintain intermediate state, or make an independent judgment is a subagent.
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Microsoft’s guidance captures the governance implication: “Delegating work to AI doesn’t transfer accountability.” The person or organization deploying the system remains responsible for reviewing, validating, and approving the result, regardless of how many agents were involved.
How do I decide which level of automation a task needs?
Evaluate the work in order, from the simplest mechanism to the most distributed one. Do not choose a multi-agent design because the task sounds sophisticated; choose it only when a smaller design cannot meet a defined requirement.
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- Describe the outcome. State what a successful result contains, who uses it, and what errors are unacceptable.
- Break the workflow into steps. Separate inputs, transformations, decisions, tool calls, approvals, and outputs. A vague request often becomes simple once its actual steps are visible.
- Score each step. Consider repeatability, impact of failure, how easily an error can be detected, and how time-sensitive the result is.
- Test deterministic methods first. If a rule, query, script, or direct model call can complete a step predictably, use it before introducing an agent.
- Test one agent next. If the remaining work needs planning or tool selection but uses one domain, one context, and one permission set, keep it together.
- Identify a real boundary. Promote a capability to a subagent only when it needs independent reasoning, separate context or memory, distinct expertise or tools, independent failure isolation, parallel execution, or a different security boundary.
- Measure the trade-off. Compare quality, latency, token and compute cost, operational effort, observability, and recovery behavior against the simpler design.
Google recommends beginning with a single agent while refining prompts, tools, and success criteria. Microsoft similarly advises running a single-agent test unless architectural separation is mandatory. Treat a multi-agent system as an optimization to prove, not a default starting point.
A practical scoring worksheet
| Question | If the answer is “yes” | Likely choice |
|---|---|---|
| Can a fixed rule, script, query, or API call produce the result? | The step is deterministic and stateless. | Code or a tool |
| Does the workflow require several tool calls or planning in one domain? | One context can hold the needed information. | One agent |
| Does a part require its own reasoning loop, memory, or model? | The capability can be specified through an interface. | Candidate subagent |
| Can that part run independently or under different permissions? | Parallelism or governance is a real requirement. | Subagent or worker |
| Will the gain exceed handoff latency, extra tokens, and maintenance? | Measured targets improve enough to justify the overhead. | Adopt the split |
Can code or a direct model call complete the task?
Use no agent when the operation is fixed, predictable, and easy to verify. Azure places direct model calls at the lowest-complexity level for this reason. Deterministic code is usually faster, cheaper, easier to test, and easier to audit than an agent making the same decision repeatedly.
Good candidates for code or tools
- Validating a document against a known schema.
- Calculating totals, converting units, sorting records, or applying a policy with explicit rules.
- Fetching a record by an identifier or running a parameterized database query.
- Transcoding media, generating a checksum, or moving files between approved locations.
- Calling a service with a stable request and response contract.
A model may still be useful before or after the deterministic step—for example, extracting fields from an unstructured message before a schema validator runs. That does not make the validator a subagent. Keep the model call and the deterministic tool separate so each can be tested against its own expected behavior.
When is one agent enough?
Use one agent when the work is multi-step but the same agent can see the relevant context, use the required tools, and apply one set of success criteria throughout. This is the normal choice for a workflow such as reading a request, querying an approved data source, checking the results, and drafting a response.
Signals that one agent is the right fit
- The workflow is sequential and manageable rather than genuinely independent.
- The same domain knowledge and tool set are needed from start to finish.
- Speed, cost, and operational simplicity matter more than parallel execution.
- The context fits within the model’s usable window without burying important instructions in noise.
- A single set of permissions is sufficient.
- Quality can be improved through clearer prompts, better tool schemas, retrieval, or validation.
A one-agent design can still have explicit stages, structured tool calls, checkpoints, and human approval. “One agent” does not mean “one unstructured prompt”; it means one reasoning owner and one working context.
Should I use a tool or spawn a subagent?
Start with a tool. Promote it to a subagent only when repeated use shows that the operation needs interpretation, planning, or isolation that a stateless function cannot provide.
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| Use a tool when… | Use a subagent when… |
|---|---|
| The operation is deterministic or narrowly bounded. | The capability must interpret ambiguity or choose among several actions. |
| It completes in one quick call. | It must plan and execute a sequence of tool calls. |
| It needs no private memory or working context. | It needs separate context, memory, or a domain-specific prompt. |
| The parent agent can directly verify the result. | It needs independent failure handling or a different completion test. |
| It uses the same model, permissions, and policies. | It requires a different model, credentials, data domain, or governance policy. |
A useful promotion test is operational: implement the capability as a tool, record where it fails or requires ad hoc instructions, and ask whether an independent reasoning loop would remove those recurring problems. If not, keep the tool.
What boundaries justify adding a subagent?
A subagent earns its place when its boundary can be stated in terms of inputs, outputs, authority, and completion checks. The following reasons are strong; “more agents seem more capable” is not.
Independent reasoning
A worker may need to explore alternatives, run a plan, inspect intermediate results, and decide when it is done without consuming the parent’s reasoning budget. Examples include a compliance analyst that tests a policy against many clauses or a debugging worker that reproduces and isolates a failure.
Separate or noisy context
Large logs, long repositories, multiple research sources, or sensitive records can pollute the coordinator’s context. A worker can inspect that material and return a bounded artifact—such as findings, citations, a patch, or a structured summary—rather than passing every raw token upstream.
Specialization
Different work may require distinct domain knowledge, tools, prompts, or models. A financial reconciliation worker, a security reviewer, and a copy editor should not automatically share the same instructions or access. Specialization is valuable when the expertise changes the quality target, not merely the label attached to the agent.
Security and governance boundaries
Separate credentials, regulated data, regional storage rules, or organizational ownership may require an isolated worker. Give the subagent only the permissions needed for its contract, and make the parent unable to bypass approval gates through an untrusted handoff.
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Parallel execution
Independent tasks can run concurrently when their outputs do not depend on one another: inspecting separate repositories, analyzing different datasets, or collecting evidence from several approved sources. Parallelism can reduce elapsed time, but only if the coordination and aggregation work are smaller than the time saved.
Dynamic routing
When the appropriate specialist is unknown at the start, a router can classify the request and hand it to the relevant worker. This pattern is justified when routing decisions are themselves stable enough to test and when each destination has a clear contract.
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When should I stay with one agent instead of splitting?
Keep the workflow in one agent when splitting would create communication without a meaningful boundary.
- Narrow scope: The task has few steps and little ambiguity.
- Shared context: Every step needs the same conversation, evidence, and intermediate reasoning.
- Strict sequence: Each action depends on the previous action, so workers cannot make progress independently.
- Uniform tools and permissions: No specialist needs a different model, credential, or policy.
- Tight latency or budget: Handoffs and extra model calls would violate the service target.
- Easy verification: A validator, schema, or human can check the single-agent result reliably.
- Unclear decomposition: You cannot define what a worker receives, returns, and is allowed to do.
Improve the prompt, tool descriptions, retrieval method, output schema, and evaluation set before adding agents. Architectural separation cannot compensate for an undefined objective.
How expensive and slow is a multi-agent design?
Every handoff can add a model call, serialized state, context packing, and waiting time. You also need tracing, retries, authentication, versioning, and a way to recover when one worker is unavailable. Anthropic reported that its multi-agent implementations typically consumed 3–10 times more tokens than single-agent approaches for equivalent tasks, in testing reported January 23, 2026. That figure is not a universal bill or latency guarantee; actual usage depends on model, prompts, context size, number of workers, and retry policy.
| Cost or risk | What creates it | How to control it |
|---|---|---|
| Token and compute use | Repeated instructions, duplicated context, worker outputs, and aggregation calls. | Pass structured artifacts, trim context, cap iterations, and route only when needed. |
| Latency | Sequential handoffs, queueing, retries, and slowest-worker effects. | Parallelize independent work, set deadlines, and return partial results safely. |
| Failure surface | Malformed outputs, unavailable workers, contradictory findings, and lost state. | Use schemas, validation, idempotent retries, and explicit fallback paths. |
| Maintenance | More prompts, tools, models, permissions, and versions to update. | Keep contracts small, assign ownership, and test each worker independently. |
| Governance | More identities and data flows to authorize and audit. | Apply least privilege, log handoffs, and require approval for high-impact actions. |
Which multi-agent architecture pattern fits the work?
Choose a pattern from the dependency structure of the task, not from its popularity.
| Pattern | Use it when | Main caution |
|---|---|---|
| Supervisor–worker | A coordinator can decompose work, assign workers, and centrally check results. | The supervisor can become a bottleneck or single point of failure. |
| Pipeline | Stages are naturally sequential and each output feeds the next stage. | One slow or bad stage delays the entire result. |
| Handoff or router | The right specialist emerges after classification or an early interaction. | Misrouting can send data to the wrong policy or tool set. |
| Parallel workers | Independent, long-running analyses can run concurrently. | Aggregation may erase disagreements or overwhelm the coordinator. |
| Hierarchical decomposition | An ambiguous, open-ended problem needs progressively smaller tasks. | Google notes that more levels increase model calls, latency, cost, and maintenance complexity. |
How should I define a subagent’s contract?
Write the contract before writing the prompt. A worker should have one objective, a bounded input, a predictable output, explicit permissions, and a completion test.
Specify the objective and scope
State what the worker must decide or produce and what it must not do. “Review these dependencies for known license conflicts” is testable; “analyze the project” is not.
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Use typed fields or a versioned schema. Include identifiers, source provenance, confidence or uncertainty where relevant, and an explicit “unable to determine” value. For large artifacts, store the file in an approved artifact store and pass a reference plus metadata instead of copying raw content through every prompt.
Set permissions and limits
List allowed tools, data domains, write operations, maximum steps, timeouts, and token limits. A worker that can only read a staging dataset should not inherit the coordinator’s production credentials.
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Define completion and escalation
Specify the checks that make the result acceptable, the conditions that require a human, and what happens on timeout or missing data. Return a structured failure rather than an invented success.
Make handoffs observable
Log the request, worker version, inputs or artifact references, tool calls, output validation, timing, retries, and final disposition. Observability lets you distinguish a prompt problem from a routing, permission, or infrastructure problem.
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Evaluation must cover the whole workflow and each worker. A passing final answer can hide an unsafe permission, an unreviewed hallucination, or a worker that succeeds only on normal examples.
Minimum acceptance checklist
- Each agent has a named owner and a measurable objective.
- Inputs, outputs, permissions, and completion checks are documented.
- Interfaces reject malformed or incomplete results before handoff.
- Every worker has a timeout, retry limit, and fallback behavior.
- High-impact actions pause for human review or a deterministic approval rule.
- Metrics cover quality, latency, token and compute use, failure rate, escalation rate, and cost per completed task.
- Logs preserve enough context to reconstruct decisions without exposing unnecessary sensitive data.
The Canadian Centre for Cyber Security recommends threat-model-based scenarios, edge cases outside normal training conditions, evaluation across autonomy levels, tests that vary tool and resource access, and tests that vary context, timing, and the presence of other agents. It also recommends continuous capability evaluations throughout the development lifecycle rather than a one-time launch test.
What does the decision look like in real workflows?
Customer-data lookup and response
Use a tool for an account lookup and a policy checker for eligibility. One agent can interpret the customer’s request, call both tools, and draft a response. Add a subagent only if, for example, a separate privacy worker must inspect a large set of records under different credentials and return a redacted summary.
Research across independent sources
If the sources are few and the context fits, one agent can retrieve, compare, and cite them. For dozens of independent repositories or long reports, parallel research workers can return bounded evidence packets to a coordinator. Require source identifiers, quotation boundaries, uncertainty flags, and deduplication before synthesis.
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Software change review
A deterministic test runner and static checks should remain tools. One agent can inspect the diff and test failures. A separate security-review worker is justified when it needs specialized rules, isolated sensitive code, or an independent approval path; its findings should be merged by a coordinator that preserves disagreements rather than averaging them away.
Open-ended planning
For a small plan, one agent is easier to steer and audit. Hierarchical decomposition can help with a large program containing distinct workstreams, but set a fixed depth, budget, and stopping rule. Otherwise the coordinator may keep creating tasks whose outputs add little information.
What commonly goes wrong, and how can I recover?
Splitting by role name instead of by capability
Labels such as “researcher,” “writer,” and “checker” do not create useful boundaries by themselves. Define the data, authority, and independent decision each role owns; merge roles that still need the same context and make the same decisions.
Passing entire conversations between workers
Full transcripts increase token use and leak irrelevant or sensitive details. Replace them with a structured brief, artifact references, required evidence, and unresolved questions.
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Workers may produce plausible but incompatible outputs. Add schemas, validation rules, confidence thresholds, and escalation states before allowing the coordinator to continue.
Letting one failure block everything
Use per-agent deadlines and explicit fallbacks. A coordinator may continue with a clearly marked partial result, retry an idempotent operation, or request human intervention; it should not silently substitute an unverified answer.
Optimizing latency while ignoring quality
Parallel workers can finish quickly and still disagree or miss the same failure. Measure accuracy, critical-error rate, and review burden alongside elapsed time.
Expanding the system without a baseline
Keep a single-agent or deterministic baseline on a fixed evaluation set. Expand only when the new architecture produces a measurable improvement in the target metric after accounting for cost and operational overhead.
What is the simplest decision tree to use?
- Can code or a direct model call complete the step 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 one agent.
- Can the proposed 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? If not, define interfaces and acceptance tests before deploying more agents.
The practical rule is to add the smallest independently testable boundary that solves a demonstrated problem. Start with deterministic components, move to one agent for adaptive multi-step work, and introduce subagents only when separation improves a measured outcome or enforces a necessary governance boundary.
Frequently Asked Questions
How many subagents should I start with?
Start with one worker at most, and only for the clearest independent boundary. Add additional workers one at a time while keeping a fixed baseline, so you can identify whether each new handoff improves quality, speed, or control.
Can subagents share the same memory?
They can, but shared mutable memory makes ownership and debugging difficult. Prefer immutable artifacts and explicit references; give a worker private memory only when retaining context is part of its defined responsibility.
When should a human approve an agent’s result?
Require approval when an action can materially affect safety, finances, legal rights, privacy, production systems, or external communications. The approval gate should receive the evidence and uncertainty needed to make a decision, not just the worker’s conclusion.
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How do I compare a multi-agent prototype with a single-agent baseline?
Run both on the same representative task set and record quality, critical errors, completion time, token or compute use, cost, retries, escalations, and human review effort. Include adversarial and edge cases, not only successful everyday requests.
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