Use separate controls for each model response, the complete agent run, and your provider account: cap output per request, track cumulative usage in your application, and set provider spend limits and alerts as backstops. Rate limits govern throughput, not how much a single task can consume. There is no universal token budget that suits every agent; start from measured workloads and test what happens when a run reaches its ceiling.
Which limits protect an AI agent?
“Token limit” can mean several different things. Match the control to the scope of the risk: a single response, an entire run, traffic over time, or billed usage over a billing period.
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| Control | Scope and meter | What it does | What it does not do |
|---|---|---|---|
| Per-request output ceiling | One model response; output tokens | Caps how much a single response can generate. | Does not cap the total work across repeated agent calls. |
| Application run budget | One task or workflow; tokens or estimated cost | Lets your application stop or redirect work when cumulative usage reaches a threshold. | Does not automatically cover delegated work unless you account for it. |
| Provider rate limit | Requests or tokens over a time window | Constrains how quickly requests can be sent or tokens processed. | Does not cap the total tokens or dollars a run can consume over time. |
| Provider spend limit | Project or organization over a billing period | Limits billed exposure at the provider level; alerts can warn before a hard limit. | Is not a precise, instantaneous per-run circuit breaker. |
For most agent services, use all four where available. A generous per-call cap plus a run ledger prevents one task from growing without bound, while provider controls limit broader exposure. Keep in mind that a provider hard cap can interrupt real service traffic; OpenAI says its enforcement may lag, allowing usage to slightly exceed the limit before it takes effect. OpenAI’s spend-limit guide describes hard limits as capable of interrupting production traffic.
Set a run budget before choosing a number
Do not pick a token ceiling from prompt length or an online “typical agent” figure. The official documentation cited here does not establish a universally appropriate token count or typical agent consumption. Derive a starting point from your own representative work, including long and failure-prone cases.
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- Define the accounting boundary. Decide whether a budget applies to one user task, a multi-step workflow, a tenant, or an agent together with its delegated agents. Give each run an ID that can be followed through retries, tool calls, and continuation.
- Choose what counts. Track model input and output usage, retries, and relevant tool-result sizes. If you set a dollar budget, record the model and cost basis as well as tokens. Document how delegated work is charged: children should use an allocated share of the parent budget or report all usage back to it.
- Measure representative runs. Log model, input and output usage, tool-result size, retries, completion outcome, latency, and estimated or billed cost for common tasks and unusually long ones. Compare distributions rather than relying on one successful run.
- Pick an initial ceiling and test it. Set per-run and per-request limits to fit the observed workload, with a deliberate margin for valid variation. Evaluate completion rate, answer quality, latency, and cost as you adjust them. A ceiling that cuts off useful work is not a successful control.
Set the boundary carefully when conversation history is resent on each API call. Providers can count repeated history differently. Anthropic documents that its task-budget countdown counts new material in the loop rather than history resent by the client; subtracting that history again in your own accounting can make Claude see an artificially depleted budget. Keep provider counters and your application ledger distinct unless their accounting rules match. Anthropic’s task-budget documentation explains this behavior.
Cap each model response
Configure the request’s maximum output using the parameter supported by your endpoint. OpenAI documents max_completion_tokens for Chat Completions and max_output_tokens for Responses. For reasoning models, the allowance includes reasoning tokens as well as visible output. Setting it too low can constrain internal reasoning or leave the answer incomplete; setting it far too high can contribute to token-rate errors, especially alongside long prompts. Check the current endpoint documentation and choose a ceiling that fits the response your task needs. OpenAI’s rate-limit troubleshooting guide covers these parameters and related errors.
A request cap is a ceiling, not a task budget. If an agent makes several calls, each can stay below its output cap while their combined use grows. The application still needs to count across the run.
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Track cumulative usage in the agent loop
Keep a run-level ledger in your orchestration code even if a provider offers a native task-budget feature. Before each model call or expensive tool, check the remaining allowance; after the operation, record actual model usage and account for tool results according to your product’s budget definition. Ensure retries and continuations carry the same run ID and cannot silently start a fresh allowance.
Define a graceful stop before the hard threshold. For example, at an earlier warning threshold, ask the agent to stop exploring and return a concise summary of completed work, unresolved items, and any partial result. At the hard threshold, stop initiating expensive work and return that partial result or a clear budget-exhausted status. Test the behavior rather than assuming the model will stop neatly.
Anthropic Claude task budgets
Anthropic documents task_budget as a beta feature for Claude Platform. Its object uses type: "tokens", a total, and an optional remaining value to carry a budget through a prior request. The budget applies across an agentic turn that may span multiple API requests and covers thinking, tool calls, tool results, and output. A fresh user message without tool results begins a new turn; tool-result messages continue the active turn. Server-side compaction during a turn does not reset the consumed budget.
The countdown is advisory and visible to the model, not exposed as a remaining-budget field in the API usage object. If your application needs its own per-run accounting, track it client-side. Review the current beta behavior and availability in Anthropic’s task-budget documentation.
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OpenAI API projects and spend limits
OpenAI supports organization and project monthly spend alerts and hard spend limits. Alerts notify you while traffic continues; a hard limit can cause affected requests to return 429 errors. Limits at both organization and project level may apply, and enforcement is not instantaneous. Use earlier alerts to trigger a human or automated response, and do not depend on the monthly hard limit to stop one runaway task. OpenAI’s spend-limit guide describes the controls and their behavior.
Projects can separate work and provide usage breakdowns, spend limits, model permissions, and rate limits. Who can manage these settings depends on organization and project roles. In the API platform, use project settings to separate development, staging, and production where practical, and confirm that both organization-level and project-level controls fit your intended exposure. See OpenAI’s guide to managing API projects.
Anthropic organization spend limits
Anthropic’s Spend Limits API is documented for Claude Enterprise organizations with usage credits enabled. Effective monthly limits can be determined by per-user overrides, group, seat tier, or organization settings. A group limit acts as a default per member; it is not one pooled allowance shared by the group. Confirm eligibility and the current configuration in Anthropic’s Spend Limits API documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep rate limits separate from budgets
Rate limits protect capacity over time. OpenAI distinguishes requests per minute from tokens per minute; these constraints vary by model and are separate from approved monthly usage limits. Anthropic documents request and token limits, remaining amounts, and reset information in API response headers, including separate input- and output-token headers. Those values help you manage throughput and decide when to wait or slow requests; they do not tell you how much of a complete task budget remains. See OpenAI’s troubleshooting guide and Anthropic’s rate-limit documentation.
When a rate limit is reached, reduce request concurrency or wait for the relevant reset rather than treating it as evidence that the run has exhausted its budget. Conversely, a run can exceed its intended total budget without ever hitting a per-minute rate limit.
Test stopping, errors, and recovery
Before deploying a budget policy, exercise the cases that can break its boundary:
- A run approaches the application threshold: verify it returns a useful partial result and stops costly work.
- A tool returns unexpectedly large output: confirm the result is bounded or accounted for before another model call begins.
- A rate limit is reached: confirm backoff and retry behavior does not multiply calls or lose the run’s accounting identity.
- A provider spend or usage limit is reached: check the error and surface an actionable pause or escalation instead of retrying indefinitely.
- A run delegates work or resumes later: ensure child agents and continuations consume the original allocation rather than starting outside it.
OpenAI distinguishes rate-limit errors from spend-limit and usage-limit errors. Retrying a billing or spend-limit error will not restore access until the underlying limit or balance is addressed. Check the error type and current provider guidance before implementing retries. OpenAI’s 429 troubleshooting guide describes common causes.
Revisit budgets when the workload changes
Re-measure after changing models, prompts, tools, delegation depth, or retry policies. Those changes can alter input size, generated output, call count, and cost. Provider pricing, rate limits, beta features, and account eligibility can also change, so confirm current settings in the linked provider documentation. A usage dashboard can help attribute consumption by model, project, or run, but observing usage is not the same as enforcing a hard per-run ceiling; keep the application ledger responsible for that boundary.
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