Control an agent team with several independent limits, not one “token budget”: cap each model response, enforce a cumulative token ceiling across the run, limit turns or tool cycles, and provide a deadline or cancellation path. Then define which exit conditions mean success, which mean an expected pause, and which mean failure. Set the numeric limits from measured usage and acceptable spend and latency; there is no universal token or turn count that fits every task.
What a token budget controls—and what it does not
An agent run can include many model calls. A response may request a tool, a tool may return data, and the agent may call the model again before producing its final result. In a team, specialist agents and a coordinating agent can add more calls. A cap on one response therefore does not, by itself, bound the total work or usage of the run.
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Separate the controls so each addresses a different failure mode:
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- Aggregate run token ceiling: limits the cumulative usage attributed to the task, including its calls and, in a team, its workers. This is an orchestrator-level control unless the provider or framework explicitly offers a whole-run limit with the semantics you need.
- Turn or cycle limit: limits repeated model decisions, tool executions, or handoffs. Define what your runtime counts as a turn: frameworks may count a model turn, a tool cycle, or another unit.
- Deadline and cancellation: bound elapsed time and let your application stop work when the user cancels, a client disconnects, or an external policy requires it. A token limit is not a timeout.
These controls complement one another. A short loop can still produce a very large response; a small output cap can still permit many calls; and a run within its token ceiling can still wait too long on a slow tool. Set and monitor the limits independently.
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First define what counts as a completed task
Before tuning numbers, write down the task’s completion contract. The orchestrator needs a reliable way to distinguish a valid answer from a tool request, an approval pause, a partial result, or a failure. OpenAI’s agent-building guide names tool calls, a specified structured output, errors, and reaching a maximum number of turns among common exit conditions. Those conditions do not all mean the same thing. OpenAI’s practical guide to building agents
Specify a success predicate
Make success something the runtime can check. For a structured task, that may mean the final response conforms to the required schema and contains the required fields. For a conversational task, it may mean the agent produced its final answer rather than requesting another tool action. Validate the result before returning it; do not infer success merely because the model call ended.
Separate pauses from completion
A request for human approval is an expected pause when the workflow requires review. Persist the run state and resume that run after approval rather than treating the pause as a completed task or silently starting a fresh task. The distinction matters for both accounting and control: the continuation belongs to the original workflow, even if it happens later.
Define failure and retry behavior in advance
Choose which tool errors, invalid outputs, guardrail interventions, or other blocked actions should terminate the run, trigger a limited retry, or be escalated. A retry consumes resources and can repeat the same failure, so retries should remain inside the same aggregate ceiling and turn policy. Make the terminal outcome visible to callers instead of returning an error-shaped or incomplete response as if it were a valid answer.
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Measure real usage before choosing numeric limits
Do not set a ceiling by counting words in a prompt or choosing a round token number. A plain-text estimate can omit cost-bearing request components. Message structure, tools, schemas, images, and files may affect input usage, and input usage does not predict how much the model will generate. OpenAI’s Help Center explicitly cautions that “A plain-text token count does not necessarily include all tokens in an API request.” OpenAI Help Center: Understanding and counting tokens
Log usage for the whole workflow
For representative successful and failed runs, record input, output, and total token usage for every model call, along with the agent or worker responsible, the task identifier, and the run outcome. Keep per-agent figures for diagnosis, but also calculate a team-wide total. Include retries and specialist handoffs in the run total; otherwise the figure understates what the task consumed.
OpenAI’s Responses usage fields include input_tokens, output_tokens, and total_tokens. Chat Completions uses prompt_tokens, completion_tokens, and total_tokens. Inspect the usage returned by the endpoint rather than assuming a local text count is complete. Field names and available details depend on the endpoint and provider, so use the chosen API’s documentation. OpenAI’s token-counting guidance
Use distributions, not one average
Review usage across representative tasks, including runs that need multiple tools, fail, or require a handoff. A single average conceals both unusually expensive cases and tasks that regularly fail at the proposed cap. Compare the distribution for successful completions with the distribution for exhausted or truncated runs, then choose limits that reflect your acceptable spend and latency while allowing the task to complete normally.
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No universal token ceiling, turn count, or stop threshold is established by the cited provider documentation. Anthropic gives a “20,000-token budget for a multihour agentic coding task” as an example of a budget that is clearly insufficient—not as a recommended setting or a measured benchmark. Anthropic’s task-budget documentation
Set four independent guardrails
1. Choose the provider’s per-call output maximum
Configure the cap for each model response using the setting supported by the selected endpoint and model. OpenAI documents max_completion_tokens for Chat Completions and max_output_tokens for Responses. Check the chosen model’s current documentation for its context window and output limit rather than assuming one model’s settings apply to another. OpenAI Help Center: Understanding and counting tokens
This setting limits an individual response, not cumulative usage across tool calls and agent handoffs. Make the runtime inspect the provider’s stop reason or equivalent response status. If the response was cut off at the output maximum, do not accept malformed structured output or unfinished prose as a successful final result.
2. Enforce an aggregate run ceiling in the orchestrator
Track usage from all calls belonging to one task and compare the running total with a run-level limit. In an agent team, account for the manager and every worker. If each parallel worker receives an independent full allowance, they can collectively exceed the budget intended for the team. A shared pool or centrally enforced sum avoids that gap.
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Providers do not all define a similarly named “budget” the same way. Anthropic describes task budgets as advisory: “Task budgets are a soft hint, not a hard cap.” A task budget may be exceeded to finish an action already in progress, while max_tokens is the enforced total-output limit and can truncate a response. Anthropic also notes that a budget that is far too small can cause early scoping or a partial result. Treat the task budget as guidance for the agent, not as the only enforcement mechanism when a strict ceiling is required. Anthropic: Task budgets
3. Bound turns, tool cycles, and handoffs
Set a maximum for the unit your orchestrator actually counts, and document that unit in the configuration. A turn ceiling is a defense against an agent that keeps requesting work, but it is not a token ceiling: a few unusually large calls may consume substantial usage, while many small calls may stay below a token limit. Choose the turn limit by observing how many cycles valid tasks need, then check whether the ceiling is cutting off normal work.
4. Add a deadline and cancellation path
Give each run an application-level deadline and a way for user or system cancellation to reach the active loop. Stop at a safe checkpoint when possible, record why the run ended, and avoid launching more tool work after cancellation has been requested. Timeouts and cancellation semantics vary by runtime; do not assume a provider’s output setting will interrupt a waiting tool or stop parallel workers.
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A practical team-level budget is an accounting rule enforced where the run is coordinated. Attribute usage to the parent task as soon as each response’s usage is available. Before dispatching more work, check the remaining aggregate allowance and whether the turn or deadline limit has been reached. Parallel calls need particular care: reserve or account for their expected work so that multiple workers do not all act on the same apparent remaining balance.
There is no source-supported universal formula for dividing a team budget between manager and workers. A fixed split may be simple to operate but can waste unused allowance or starve a worker with a legitimate subtask. A shared pool is more flexible but still needs central enforcement and visibility into each worker’s usage. Choose the allocation method that fits the runtime, and alert when one agent repeatedly dominates consumption.
Keep conversation-state handling consistent as well. OpenAI’s running-agents guide recommends choosing one state strategy in most applications; mixing local replay with server-managed state can duplicate context. Duplicated context can inflate input usage and make observed run totals harder to interpret. OpenAI: Running agents
Decide what each stop condition means
Stopping is not the same as succeeding. Return an explicit status that tells the caller whether the task completed, paused, exhausted a resource limit, was cancelled, or failed. The following matrix is a design guide; exact response fields and runtime behavior depend on the provider and framework.
| Condition | Interpretation | Orchestrator action |
|---|---|---|
| Valid final output passes the completion predicate | Successful completion | Validate and return the result. |
| Human approval is required | Expected pause | Persist state and resume the same run after approval. |
| Tool call or handoff is requested | Work continues unless configured as terminal | Execute or route the action, then evaluate the run again. |
| Aggregate token ceiling or turn limit is reached | Graceful resource exhaustion | Return a bounded partial status or deliberately resume with a new budget. |
| Per-call output cap is reached or response is truncated | That response may be incomplete | Do not silently accept it; classify it as truncation or failure and apply the defined recovery policy. |
| Deadline, client disconnect, or user cancellation | External cancellation | Stop at a safe checkpoint and record the cancellation reason. |
| Tool error, guardrail intervention, or invalid output | Failure or blocked action | Apply the explicit retry, escalation, or terminal-error policy. |
Frameworks may expose more specific stop reasons. Strands Agents, for example, documents reasons including end turn, tool use, cancellation, turn limit, total-token limit, output-token limit, max-token truncation, stop sequence, content filter, and guardrail intervention. Its documentation treats limit exhaustion as graceful and max-token truncation as unrecoverable within that loop. Those are Strands-specific semantics; another runtime may return different statuses or require different handling. Strands Agents: Agent Loop
Reserve enough capacity to report exhaustion clearly
If the run is nearing its aggregate ceiling, allow the orchestrator to ask for a concise status while there is still room for a response. That status can identify completed work, what remains, and the next action. This is an implementation pattern, not a vendor-guaranteed resume protocol; a partial status should be labeled as partial rather than presented as the task’s final answer.
Keep enough output allowance available for the status or final response instead of spending the entire run budget on another exploratory call. How much to reserve depends on the task and response format, so measure it. If the runtime cannot make a final call inside the remaining ceiling, report exhaustion through the application rather than pretending the agent completed the work.
Quick Recap
Tune limits without hiding incomplete work
- Run representative tasks with logging enabled. Capture call-level and team-level usage, turn counts, elapsed time, stop reasons, and whether the completion predicate passed.
- Set an initial ceiling from observed successful runs. Select limits according to the acceptable cost and latency for the task, with room for normal variation. Do not treat one unusually large run as the default requirement without examining why it was large.
- Exercise each exit path deliberately. Verify that a normal final answer succeeds, a required approval pauses and resumes, a tool failure follows policy, a capped response is rejected as incomplete, and token, turn, timeout, and cancellation limits produce distinguishable outcomes.
- Review failures and near-limit runs. If caps repeatedly prevent ordinary tasks from completing, determine whether the limit is too low or the workflow is wasteful before changing it. Look for duplicated context, repeated tool results, unnecessary calls, and handoffs that do not add value.
- Reduce avoidable input before raising limits. OpenAI recommends shortening prompts, removing repeated context, dividing oversized inputs, or summarizing and preprocessing material. These changes can reduce needless usage without forcing a smaller result. OpenAI Help Center: Understanding and counting tokens
- Recheck after workflow changes. New tools, schemas, models, worker roles, or state-handling changes can alter usage and stop behavior. Re-measure rather than carrying forward a budget whose assumptions no longer fit.
Common budget mistakes
- Using an output cap as the whole-run budget. A per-response limit does not stop a long sequence of model calls. Keep a separate cumulative ceiling.
- Counting only the coordinator. Worker calls, retries, and parallel tasks belong to the same team-level accounting if they serve one task.
- Trusting a local text estimate as exact API usage. Request structure and multimodal inputs can affect usage. Inspect the endpoint’s returned usage fields.
- Treating exhaustion as success. A graceful stop can still leave the requested work incomplete. Return the status and partial-result state accurately.
- Retrying without charging the same budget. Retries that reset accounting can turn a protective ceiling into an unlimited allowance.
- Equating similar provider settings. An advisory task budget, an enforced per-response maximum, a loop-level ceiling, and a framework stop reason are different controls. Read the chosen provider’s semantics rather than transferring assumptions between systems.
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