The Tool Desk
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Which limits do you need?
These controls apply at different scopes. A per-request setting cannot contain a loop that makes many calls, while a monthly spend limit is too broad to act as a precise per-run circuit breaker.
| Control | Scope and meter | What it does | What it does not do |
|---|---|---|---|
| Output ceiling | One model request; output tokens | Caps the output allowance for a response. | Does not cap the total work of an agent that makes additional requests. |
| Run-level budget | One application-defined task or workflow; tokens or estimated cost | Lets your application account for cumulative work and stop or change behavior at a threshold. | Does not automatically cover delegated agents unless you allocate their work to the same budget. |
| Provider spend limit | Project or organization; billed usage over a billing period | Provides a backstop against usage exceeding a broader spending ceiling. Alerts can give advance notice. | Is not a precise per-task limit, and enforcement can lag. |
| Rate limit | Provider account or model; requests or tokens over a time window | Constrains throughput and helps manage how quickly work is sent. | Does not set the total tokens or dollars an agent can consume during a run. |
For OpenAI, the request-rate and token-rate limits are distinct from approved monthly usage limits. Anthropic documents request, total-token, input-token and output-token rate-limit headers. Those headers report throughput constraints and remaining capacity, not the remaining budget for a task. See OpenAI’s rate-limit guidance and Anthropic’s rate-limit documentation.
What token budget should you set for an AI agent?
Choose a starting ceiling from your own representative runs rather than adopting a token number from another workload. Prompt length alone is a poor predictor: tool results, retries, model choice, and the number of steps can all affect usage. Official OpenAI and Anthropic documentation does not establish a universal budget or typical token consumption that fits all agent tasks.
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- Run representative short, typical, and unusually long tasks using the models, prompts, tools, and delegation patterns you expect in production.
- For each run, record input and output usage, model, retries, tool-result sizes, completion outcome, and estimated or billed cost.
- Choose an initial per-run ceiling that accommodates the work you intend to complete while containing the cost of an unexpectedly long run.
- Test outcomes near the ceiling: whether the task completes, returns a useful partial result, stops clearly, or needs a continuation path.
- Re-measure after changing models, prompts, tools, delegation depth, or retry behavior; those changes can alter the run’s usage profile.
Evaluate more than token consumption. Track completion and output quality alongside latency and cost, so a lower budget does not quietly turn successful tasks into incomplete answers. The appropriate trade-off depends on the task and service.
How to implement a per-run budget
Define what one budget covers
Give each task or workflow a run ID and define its accounting boundary: for example, one user task, a complete workflow, or all work performed for a tenant. Decide whether the ceiling includes every model call, retries, tool results, and delegated agents. Without an explicit boundary, two parts of the system may count different work as the same run—or fail to count it at all.
If agents can delegate, assign children a share of the parent’s remaining budget and charge their usage back to that run. This is an application design recommendation, not a universal provider algorithm; the goal is to ensure concurrent child work cannot escape the parent task’s ceiling.
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Keep a usage ledger and enforce it before work
Before each model request or costly tool call, check the run’s remaining allowance. After work completes, reconcile actual usage and charge it to the same run. Include retries and tool-result sizes in your accounting policy, and make sure error handling cannot start a new loop outside the original budget.
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Define a graceful stopping path before setting a hard threshold. For example, as the run approaches its limit, ask the agent to return a concise partial result or summarize completed work rather than beginning another expensive step. Treat this as a deliberate product behavior: an application hard stop may protect spend, but can leave a task unfinished.
Set a per-request output ceiling
Limit the output allowance on each model call as a separate safeguard. OpenAI documents max_completion_tokens for Chat Completions and max_output_tokens for Responses. Use the parameter supported by the endpoint you call; details are in OpenAI’s rate-limit and 429 guidance.
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For reasoning models, OpenAI says the allowance includes reasoning tokens as well as visible output. Setting it too low can constrain reasoning or result in incomplete work, so select the ceiling based on the expected response and test it. OpenAI also notes that long prompts and large output allowances can contribute to token-rate errors. A request cap still does not limit the cumulative usage of an agent that makes additional calls.
Consider a native task budget for Claude
Anthropic documents task_budget as a beta Claude Platform feature for an agentic turn spanning multiple API requests. It accepts an object with type: "tokens", a total, and an optional remaining value to carry the budget through a prior request. Anthropic says the budget covers thinking, tool calls, tool results, and output. See Anthropic’s task-budget documentation for the current feature details and availability.
The budget helps Claude self-regulate, but it is advisory rather than an application hard stop. Its remaining countdown is not returned as an API usage field, so keep your own client-side usage ledger if your application needs to enforce a threshold. Anthropic says a fresh user message without tool results starts a new turn, while tool-result messages continue the active turn; server-side compaction during a turn does not reset its consumed budget.
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Take care when carrying the budget forward. Anthropic says its countdown counts new material in the loop rather than conversation history resent by the client. Subtracting resent history again can make the model see an artificially depleted budget.
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OpenAI projects and organizations
OpenAI documents monthly spend alerts and hard spend limits at organization and project level. Alerts notify while traffic continues; a hard limit can cause affected API requests to return 429 errors. Both organization and project limits may apply, and enforcement is not instantaneous: recorded usage may slightly exceed the configured amount while limit state propagates. Do not rely on this control to stop one run exactly at a chosen cost. See OpenAI’s spend-limit guide.
Projects can organize work with usage breakdowns, model usage permissions, rate limits, and project spend limits. Separate development, staging, and production projects where practical so usage and settings are easier to manage. The available controls depend on account roles: project and organization owners manage different settings. See OpenAI’s project-management guidance.
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Anthropic Enterprise spend limits
Anthropic’s Spend Limits API documentation applies to Claude Enterprise organizations with usage credits turned on. Effective monthly limits may be determined by per-user overrides, group settings, seat tier, or organization settings. A group limit is a default per member, not one pooled allowance shared by the group. Confirm eligibility and current behavior in Anthropic’s Spend Limits API documentation.
Plan for limits, errors, and recovery
Test each boundary before relying on it in production. Exercise a run approaching its application budget, a provider rate-limit response, a spend-limit response, and a tool that returns unexpectedly large output. Check that retries, continuation, or escalation do not restart work outside the same run budget.
- Application run budget reached: Stop or switch to the planned partial-result path, and record why the run ended.
- Rate limit reached: Treat it as a throughput constraint, not proof that the run has exhausted its total budget. Consult the provider’s current rate-limit guidance before deciding how to resume.
- Provider spend limit reached: OpenAI distinguishes spend-limit and usage-limit errors from rate-limit errors. Retrying a billing or spend error does not restore access until the underlying limit or balance is addressed.
- Unexpectedly large tool result: Account for the result under your run policy and test whether it should be bounded, summarized, or rejected before another model call.
A provider hard cap can interrupt real service traffic; OpenAI explicitly warns, “Hard spend limits can interrupt production traffic.” Use alerts and plan a response path rather than treating the cap as a substitute for application-level controls.
Quick Recap
What to review as your agent changes
- Re-measure after changing the model, prompt, tool outputs, retry policy, or delegation behavior.
- Confirm the run boundary still includes all intended calls and delegated work.
- Check current provider documentation for rate limits, billing eligibility, beta features, and console settings, which can change.
- Use usage-monitoring tools, if needed, to attribute spend by run, model, or project; a dashboard can provide visibility but may not enforce a hard per-run cap.
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