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To control an AI agent team’s spend and prevent runaway or incomplete runs, use several independent safeguards: a per-response output cap, an aggregate token budget for the entire run, a limit on model turns or tool cycles, and a deadline or external cancellation mechanism. Measure representative tasks before choosing numeric limits. A provider’s output cap usually limits one response, not all the calls made by a multi-step agent loop.
What a token budget needs to control
An agent run may call a model repeatedly, execute tools, hand work to specialist agents, and then call a model again with tool results. A limit on one response cannot by itself put a ceiling on that whole process. A useful design separates four controls and defines what the system should do when each one is reached.
| Control | What it limits | Why it matters |
|---|---|---|
| Per-response output cap | The maximum output for an individual model response, as defined by the provider. | Prevents one response from growing without limit. It does not bound input tokens or cumulative usage across later calls. |
| Aggregate run token budget | The combined token usage of the manager and all workers participating in one run. | Controls cumulative usage across model calls, handoffs, and retries. Implement this at the orchestrator level unless the provider explicitly offers the required whole-run control. |
| Turn or cycle limit | The number of model turns, tool cycles, or handoffs the orchestrator permits. | Provides a separate bound when a task makes many small calls or an agent repeatedly revisits the same work. |
| Deadline and cancellation | Elapsed runtime or an external stop signal, such as a user canceling or a client disconnecting. | Stops work that consumes too much time, even if it remains within token and turn limits. |
These controls are not interchangeable. A short deadline may stop a useful task before it finishes; a high turn cap does not limit the size of each response; and a per-call output cap cannot prevent a long sequence of individually small calls. Keep the controls independent and record which one ended each run.
Measure actual usage before choosing numbers
There is no universal token budget or turn count that fits every task. The appropriate limits depend on what a valid result requires, the task’s measured usage, acceptable spend, and the latency the application can tolerate. Official documentation reviewed for this guide does not establish a general-purpose numeric budget or stop threshold.
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Count the complete request, then inspect endpoint usage
Counting the visible words in a prompt is not a reliable estimate of all input tokens. Message structure, tool definitions, schemas, images, files, and other multimodal content can affect request usage. OpenAI’s Help Center cautions that “A plain-text token count does not necessarily include all tokens in an API request.” For Responses inputs, use complete-input counting where available and inspect the usage fields returned after actual calls. Responses reports input_tokens, output_tokens, and total_tokens; Chat Completions reports prompt_tokens, completion_tokens, and total_tokens. See OpenAI’s guide to understanding and counting tokens.
Input counts do not predict output counts. Track both, along with total usage, for the actual model and request shape used in production. If a run calls more than one model or delegates to workers, collect usage per call and per agent as well as for the run as a whole.
Use representative runs, not one convenient average
Exercise the range of tasks the system is expected to handle, including ordinary requests, complex cases, tool errors, retries, and handoffs. Review successful and unsuccessful runs separately: a low average can conceal occasional high-usage tasks, while a budget set from a worst-case outlier can waste resources on every ordinary task. Compare the observed distribution with your acceptable cost and latency, then test whether the proposed limits allow normal completion while containing atypical runs.
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When runs use more tokens than expected, inspect the trace before raising the ceiling. Repeatedly supplying the same context or returning large tool results to the model can increase input usage on later calls. OpenAI’s guidance recommends shortening prompts, removing repeated context, dividing oversized inputs, or summarizing and preprocessing them. State management matters too: OpenAI’s running-agents guide recommends choosing one conversation-state strategy in most applications, because mixing local replay with server-managed state can duplicate context. See OpenAI’s running-agents guide.
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Set a contract for completion and stopping
Before setting limits, define what counts as a completed task. “The model stopped responding” is not a completion rule. Specify the valid final output, the conditions that require human approval, which tool calls or handoffs continue the task, and how errors or invalid results should be handled. OpenAI’s practical guide lists tool calls, a particular structured output, errors, and a maximum number of turns among common exit conditions. The exact behavior depends on the application and runtime. See OpenAI’s practical guide to building agents.
Make the run state distinguish at least these outcomes:
- Completed: a final answer or configured structured result passes validation.
- Awaiting approval: the run is paused for an expected human decision and can continue from its saved state.
- Continuing: the model requested a tool or handed work to another agent; execute or route that work and evaluate the run again.
- Resource-limited: the aggregate token or turn ceiling was reached before a valid final result.
- Failed or blocked: an error, invalid output, guardrail intervention, or other disallowed condition needs an explicit retry, escalation, or terminal-error policy.
- Cancelled: a deadline, user action, client disconnect, or other external signal stopped the run.
This distinction prevents a partial response, an approval pause, or a tool handoff from being mistaken for successful completion. OpenAI’s running-agents guidance distinguishes expected human approvals from runtime or validation failures such as maximum-turn limits, guardrail exceptions, and tool errors.
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1. Set the provider’s per-response output cap
Use the output-limit setting supported by the provider and endpoint in use. OpenAI documents max_output_tokens for Responses and max_completion_tokens for Chat Completions. Check the selected model’s documentation for its context window and output limit; do not assume one model’s settings or limits apply to another. The OpenAI Agents SDK also exposes model settings for output limits and tool behavior in its model settings reference.
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A response that reaches its output cap may be incomplete. Treat the provider’s stop reason as a signal to validate the result, not as proof that the task finished. For structured output, check that the response is complete and valid before returning it. A truncated response may require a deliberate recovery path, such as reporting that the result is incomplete or restarting with a suitable budget; silently accepting malformed partial output can turn a resource limit into a user-facing error.
2. Enforce an aggregate budget across the whole team
Have the orchestrator account for all model usage associated with a run, including the manager, specialist workers, retries, and any follow-up calls. This is an implementation recommendation based on usage inspection and documented multi-agent orchestration patterns: the cited frameworks do not prescribe a universal worker-allocation formula. If each worker receives an independent ceiling, several workers can all remain within their own limits while the team exceeds the intended total.
Two practical designs are a shared pool, where every call draws from one run-level balance, or an aggregate ceiling that sums recorded usage centrally. A shared pool can use remaining capacity flexibly; fixed per-worker allocations make individual limits easier to reason about but may leave unused capacity in one branch while another branch needs it. Whichever design you choose, define what counts toward the team total and verify that every participating model call is recorded. Do not infer aggregate usage from the manager’s calls alone.
3. Add a turn or tool-cycle limit
Set a separate maximum for model turns or orchestration cycles. Decide precisely what the runtime counts: for example, whether a turn means one model response, or whether a tool execution and handoff also count as a cycle. Consistent definitions make logs and limit behavior interpretable. A turn limit catches loops that use many small calls, while the token ceiling catches calls that are large. Both are needed to bound different failure modes.
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4. Add a deadline and cancellation path
Enforce an application-level deadline and propagate cancellation to active model calls, tools, and workers where the runtime permits it. Define safe checkpoints at which the orchestrator can stop dispatching new work, preserve useful run state, and record the cancellation reason. Timeouts, user stops, and client disconnects are external cancellation events—not evidence that the task completed. The runtime should make clear whether cancellation interrupts an active call immediately or takes effect after that call returns.
Understand provider-specific budget semantics
Settings with similar names can have different scopes and enforcement behavior. Compare whether a control applies to one response or the whole loop, whether it is advisory or enforced, what kinds of input count, and what the runtime returns when the limit is reached.
| Provider or framework behavior | What the documentation establishes | Implementation implication |
|---|---|---|
| OpenAI output settings | Responses uses max_output_tokens; Chat Completions uses max_completion_tokens. Model-specific context and output limits still apply. |
Use the setting for an individual response, then separately enforce a cumulative run budget and inspect usage. |
| Anthropic task budgets | Task budgets are advisory: Anthropic says, “Task budgets are a soft hint, not a hard cap.” They can be exceeded to finish an in-progress action. max_tokens is the enforced total-output limit and can truncate. |
Do not treat a task budget as a strict bill ceiling. Handle output truncation separately from graceful task-budget exhaustion. Anthropic notes that a budget that is too small can lead to early scoping or a partial result; its example of a 20,000-token budget for a multihour coding task illustrates insufficiency, not a recommended threshold. See Anthropic’s task-budget documentation. |
| Strands agent loop | Documented stop reasons include end turn, tool use, cancellation, turn limit, total-token limit, output-token limit, max-token truncation, stop sequence, content filter, and guardrail intervention. | Use the reported stop reason to distinguish graceful limit exhaustion from truncation and other failure or blocked states. These are Strands-specific semantics, not a guarantee of other runtimes. See Strands’ agent-loop documentation. |
Anthropic’s example is useful precisely because it is not a general target: the right budget depends on the work. A soft task budget that allows an in-progress action to finish may help avoid an abrupt stop, but if the application needs a strict maximum, enforce an aggregate ceiling in the orchestrator as well.
Handle each stop condition deliberately
| Stop condition | Interpretation | Recommended handling |
|---|---|---|
| Valid final output or completion predicate | Successful completion, subject to validation. | Validate the result and return it. |
| Human approval required | Expected pause, not success or failure. | Persist the run state and resume the same task after approval; do not treat the pause as a new task. |
| Tool call or handoff | Work continues unless configured as a terminal action. | Execute the tool or route the task to the specialist, record usage, then evaluate the run again. |
| Aggregate token or turn ceiling | Graceful resource exhaustion; the intended work may be incomplete. | Return a clearly labeled bounded partial result and status, or resume deliberately with a newly approved budget. |
| Per-call output cap or truncation | The individual response may be incomplete. | Inspect the stop reason and validate the response. Do not accept malformed or incomplete structured output as success. |
| Timeout, client disconnect, or user stop | External cancellation. | Stop at a safe checkpoint, cancel outstanding work where possible, and record why the run ended. |
| Tool error, guardrail intervention, or invalid output | Failure or blocked action. | Apply an explicit retry, escalation, or terminal-error policy; do not classify it as a successful final result. |
Exact stop-reason names and recovery behavior vary by SDK and provider. In particular, do not assume that a framework which returns a graceful partial result on a total-token limit will do the same for a per-response truncation.
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Reserve room for a useful ending
If the orchestrator waits until the aggregate ceiling is fully consumed before trying to produce a status, the final reporting call may have no capacity left. Track remaining budget and, as it approaches the limit, stop launching optional work and ask for a concise status, partial result, and next action while output capacity remains. The aim is to give the caller a truthful account of what is complete, what is not, and whether a deliberate continuation is possible.
This is an implementation pattern, not a vendor-guaranteed resume protocol. Provider task-budget behavior may support finishing an in-progress action, but it does not guarantee that a run can be resumed in a particular way. Persist whatever state your application needs to continue, and treat any new budget as an explicit decision rather than silently resetting the original run’s ceiling.
A practical rollout sequence
- Define completion and failure states. Specify the final output or predicate that proves completion, approval pauses, permitted handoffs, and error behavior. Make each outcome visible in the run record.
- Instrument complete usage. Record input, output, and total tokens for every model call, then aggregate by run and agent. Include the request types used by the application and inspect provider-reported usage rather than relying only on visible-text estimates.
- Choose limits from observed tasks. Set an individual output cap, a team-wide cumulative ceiling, a maximum turn or cycle count, and a deadline according to measured usage and acceptable spend and latency. There is no documented universal numeric setting to copy.
- Specify exhaustion behavior. Decide what the application returns on a run-level ceiling, a turn limit, output truncation, timeout, cancellation, invalid output, and tool failure. Keep partial, failed, paused, cancelled, and completed states distinct.
- Test the boundary cases. Run normal tasks and cases with repeated tool use, handoffs, retries, slow tools, invalid responses, and cancellation. Confirm that aggregate accounting includes every worker and that the intended stop reason reaches logs and callers.
- Tune based on outcomes. If a cap repeatedly blocks otherwise valid tasks, revisit the task design and measured budget. If usage is wasteful, reduce duplicated context or unnecessary tool output before raising limits. Re-test after changing prompts, tools, models, or orchestration because usage can change.
What to inspect when runs behave badly
- The bill grows despite a low output cap: check the number of calls, input usage on later calls, worker usage, and retries. A per-response cap does not limit cumulative input or total run usage.
- The run hits a turn limit but has tokens left: inspect whether the agent is repeating tool cycles or spending turns on coordination. Tighten the completion predicate or simplify handoffs rather than assuming a larger token ceiling will fix the loop.
- The run reaches its token ceiling with no useful final status: reserve completion capacity and stop optional work before exhausting the pool.
- Responses are cut off: distinguish a provider output cap or max-token truncation from an aggregate budget stop. Validate output and revise the per-call limit or requested response size when measurements justify it.
- Team usage exceeds the planned ceiling: verify that the orchestrator aggregates managers, workers, and retries instead of giving each an uncoordinated independent allowance.
- Usage suddenly increases after a state-management change: inspect whether context is being replayed in addition to being managed server-side. Duplicate conversation history can make later requests larger.
Choosing a framework or provider control
Before relying on any named “budget” or “limit” setting, verify its exact semantics in the provider or framework version you deploy. Compare these properties rather than the setting names:
- Is the limit advisory or hard enforced?
- Does it cover one model response or the entire agent loop?
- Which input components count, including tool results, schemas, reasoning, images, and files?
- Are turn, output, total-token, timeout, and cancellation controls all available?
- On exhaustion, does the runtime return a partial result, truncate output, raise an exception, or preserve resumable state?
- Can usage from managers and workers be observed and aggregated centrally?
The same label can mask different behaviors, so write down the exact provider semantics beside the application’s own policy. Use provider limits for the specific calls they govern, and use the orchestrator for team-wide controls that span calls and workers.
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