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The reliable way to stop an AI web agent from browsing forever is to enforce separate ceilings for money, model tokens, requests, browser actions, network bytes, and elapsed time. Put those checks in an orchestrator that runs before every model or tool call, then add provider rate limits, a hard spend limit, and a controlled browser network. When a ceiling is reached, pause or terminate the run and record the exact reason.
No single limit is enough. A token cap will not stop a loop that keeps making cheap browser calls; a request cap will not prevent one enormous search response; and a provider-wide rate limit cannot tell you which task consumed the budget. Treat each as an independent circuit breaker.
Start with six independent ceilings
Define the limits before an agent starts. Store them with the run so that a retry, child agent, or parallel worker cannot silently receive a larger allowance.
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| Ceiling | What to count | Check timing | When exhausted |
|---|---|---|---|
| Money | Estimated and billed API cost for the run | Before a call using the latest usage and price data; reconcile after the response | Stop, or require an explicit budget increase |
| Model tokens | Input, output, reasoning (when exposed), cached tokens, and total tokens | Before each model call and after usage is reported | Refuse the next call or switch to a lower-cost policy |
| Requests | Model calls, search calls, browser-tool calls, and other API requests | Atomically before dispatch | Pause or terminate with a request-budget code |
| Browser actions | Navigations, clicks, form submissions, scrolls, waits, and screenshots | Before every action | Stop the browser loop and preserve the page state |
| Network bytes | Bytes sent and received, including page assets, downloads, uploads, and screenshots | At the proxy/runtime boundary and after each action | Block the transfer or end the run |
| Time | Wall-clock duration and idle time | Continuous watchdog, not only between calls | Cancel outstanding work and terminate |
Use a small safety margin for estimates. A model may return more output than expected, and a browser action can trigger redirects or image loads after the tool call appears complete. The enforcement point must be authoritative: counters in a prompt or a dashboard are useful for visibility but do not constrain execution.
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Build the orchestrator as the enforcement point
The orchestrator owns the run state and is the only component allowed to dispatch model or browser work. Every worker, tool, and sub-agent requests a lease from it. A lease reserves budget before dispatch, preventing two parallel calls from both spending the same remaining allowance.
Keep a per-run ledger
Persist a ledger keyed by a run identifier. Include the configured ceiling, reserved amount, actual amount, and timestamp for each counter. Track at least:
- input, output, reasoning, cached, and total model tokens when the provider reports them;
- model calls, search calls, browser-tool calls, and all other outbound requests;
- browser steps, navigations, screenshots, downloads, uploads, and redirects;
- bytes sent and received at the network boundary;
- elapsed time, cancellation state, and the identity of the worker that requested each action.
Use an atomic transaction or centralized counter service for reservations. If a reservation cannot be written, fail closed rather than dispatching an unmetered call.
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Before dispatch, calculate the worst-case reservation for the operation. For a model call, reserve the maximum permitted output and the expected input; for a screenshot or download, reserve the configured byte allowance. After completion, replace the reservation with the provider’s actual usage and release the difference. If the provider reports usage asynchronously, keep the reservation until reconciliation.
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function authorize(run, operation):
usage = ledger.read(run.id)
if usage.time_seconds >= run.limits.time_seconds:
stop(run, "TIMEOUT")
if usage.requests + operation.request_reserve > run.limits.requests:
stop(run, "REQUEST_BUDGET")
if usage.total_tokens + operation.token_reserve > run.limits.total_tokens:
stop(run, "TOKEN_BUDGET")
if usage.browser_steps + operation.step_reserve > run.limits.browser_steps:
stop(run, "BROWSER_STEP_BUDGET")
if usage.bytes_received + operation.byte_reserve > run.limits.bytes_received:
stop(run, "BYTE_BUDGET")
if usage.estimated_cost + operation.cost_reserve > run.limits.money:
stop(run, "RUN_SPEND_BUDGET")
ledger.reserve(run.id, operation)
return ALLOWEDMake stop idempotent. It should cancel queued work, revoke tool leases, close or freeze the browser, and emit one final termination record. A pause state is appropriate when a person can raise the limit; termination is safer for unattended jobs.
Prevent bypasses from parallelism
Give child agents a sub-budget deducted from the parent before they start. The parent must reserve the maximum child allowance, not merely pass a verbal instruction. Apply the same rule to retries: a retry consumes a new request and its own token and byte reservations. Never let a tool call the provider directly with credentials that bypass the orchestrator.
Use provider limits as a backstop, not as a task budget
Provider limits protect an organization or project from traffic spikes. They do not express “this research task may spend 80,000 tokens” and they do not identify which run caused a failure. Keep them in addition to the orchestrator ledger.
| Scope and model group | Requests per minute | Tokens per minute | Qualification |
|---|---|---|---|
| Build-tier Astra, Sol, and Terra models | 5,000 | 1,000,000 | OpenAI’s standard 2026 limits table; limits vary by tier and model |
| Build-tier Luna | 5,000 | 2,000,000 | OpenAI’s standard 2026 limits table; limits vary by tier and model |
These RPM and TPM values are organization- or project-level throughput ceilings, not recommendations for a single run. A fleet can reach them with many concurrent jobs even when each job has a modest allowance. Add a concurrency semaphore in the orchestrator and reserve capacity before dispatch so bursts do not turn into provider errors.
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Configure a hard spend limit
Use a spend alert for visibility and a hard spend limit for enforcement. OpenAI’s rate-limit documentation distinguishes them: a spend alert sends a notification while API traffic continues; a hard spend limit causes affected API requests to return HTTP 429. Limits are separate from approved monthly usage limits, and propagation can allow a small amount of extra usage to be recorded after the threshold is crossed. Therefore, keep per-run limits below the organizational hard limit and treat the 429 response as an emergency backstop, not normal control flow.
When a 429 arrives, classify it separately from a per-run stop. Retry only according to the provider’s guidance and only if the run still has request, token, time, and money headroom. Blind retries can convert a rate-limit incident into a spend incident.
Control web-search payloads and long jobs
Hosted web search can return a large amount of text even when the agent makes only one search call. Set a finite return_token_budget for each search operation and include that reservation in the run’s token and money checks. OpenAI’s web-search documentation warns that unlimited can increase latency and cost; use it only for an intentionally high-effort run with an explicit ceiling elsewhere.
Choose a search policy
- Set a per-search result-token ceiling and a smaller total search-payload ceiling for the run.
- Restrict domains when the task permits. This reduces irrelevant pages and makes byte use more predictable.
- Limit the number of result pages the agent may open from one query.
- Require a reason code before a second search query, such as “missing primary source” or “conflicting evidence.”
- Summarize or compress retrieved content once, then pass the compact representation to later steps instead of repeatedly attaching full pages.
Use background execution deliberately
Background mode is useful for long jobs only when cancellation and budgets are explicit. Give the job a wall-clock deadline, a maximum number of polling requests, and a persisted cancellation token. A background flag by itself does not stop an agent that continues searching after the useful result is complete.
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Constrain the browser and network runtime
Browser bandwidth is runtime-dependent. Official sources do not publish a universal byte-per-run allowance for browser agents or a standard industry bandwidth figure. Measure traffic in the environment you operate, then choose a policy based on page types, screenshot frequency, downloads, and concurrency.
Use an allowlist and an egress gateway
Run the browser in an isolated environment whose egress passes through a proxy or gateway you control. Allow only the domains and protocols required for the task. Record DNS lookups, redirects, response sizes, status codes, and cache hits. Block direct network access from helper processes so a tool cannot evade the byte counter.
Cap expensive browser operations
- Set maximum navigations, clicks, scrolls, waits, and screenshots per run.
- Apply a byte limit to each response and a separate cumulative receive limit.
- Block large files, video, software installers, and archive downloads unless the task explicitly requires one. If a download is required, authorize its size before starting it.
- Disable unneeded images, fonts, advertisements, and autoplay media where doing so does not change the evidence the task needs.
- Deduplicate URLs and cache pages within a run. Charge a cache miss and a fresh screenshot; do not charge the same stored response repeatedly.
- Set a maximum redirect chain and reject redirects to domains outside the allowlist.
Measure screenshots separately
Screenshots can dominate both bandwidth and token use when they are repeatedly sent to a vision-capable model. Set a screenshot count and a per-image byte ceiling. Prefer a cropped region or structured page extraction when visual context is not necessary. The browser controller should reject a screenshot request that would exceed either the image budget or the run’s remaining byte allowance.
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Budget controls limit cost, not harm. Require a human confirmation before purchases, account changes, form submissions with legal or financial effect, deletion, publication, or other destructive actions. Keep the confirmation outside the model’s text channel: the orchestrator should receive a signed approval tied to the exact action, target, and expiry time.
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OpenAI’s computer-use guidance says that if confirmation must be guaranteed, restrict the hosted browser to resources that cannot perform consequential actions or use a browser runtime you control. In practice, maintain separate read-only and action-enabled environments, and give the agent credentials only after approval. A token or byte budget should never be treated as permission to act.
Log why every run stopped
Persist a machine-readable termination code with the final ledger. Useful codes include TOKEN_BUDGET, REQUEST_BUDGET, SEARCH_PAYLOAD_BUDGET, BYTE_BUDGET, BROWSER_STEP_BUDGET, TIMEOUT, RUN_SPEND_BUDGET, SPEND_LIMIT, RATE_LIMIT, HUMAN_REQUIRED, and COMPLETED.
For each call, log the run and worker identifiers, operation type, requested reservation, actual usage, provider request identifier, start and end times, destination domain, HTTP status, and remaining budget. Redact prompts, cookies, authorization headers, and personal data according to your retention policy. The Agents SDK automatically tracks usage for every run, including requests, input tokens, output tokens, total tokens, cached-token details, and reasoning-token details; export those fields into the same ledger rather than maintaining a second, conflicting count.
Set budgets with a repeatable policy
Do not pick one allowance for every task. Classify jobs by work type and assign ceilings that reflect their expected page and model behavior.
| Job profile | Typical policy shape | Useful additional guard |
|---|---|---|
| Single-site fact lookup | Low search-result tokens, few navigations, one or two screenshots | Stop after the target page and one fallback source |
| Multi-source research | Higher search and model-token ceilings, moderate navigation and byte limits | Require a justification for each new query or domain |
| Visual workflow | Lower text-search allowance but a defined screenshot and action budget | Charge every screenshot and enforce a maximum image size |
| Long background investigation | Explicit time, polling, search-payload, and spend ceilings | Persist cancellation state and checkpoint results |
Example of a per-run policy
The following is an illustrative policy, not a universal allowance: 100,000 total model tokens, 200 outbound requests, 80 browser steps, 50 MB received, 5 MB sent, 12 screenshots, 30 minutes elapsed time, and a fixed monetary ceiling approved for that job. The values should come from your own measurements. Start conservatively, inspect completed-run distributions, and raise only the specific ceiling that is routinely reached.
Reserve the search payload separately inside the token budget—for example, a run may have 100,000 total tokens but only 20,000 tokens available for search results. This prevents a single broad query from consuming the context needed to analyze the evidence.
Compare enforcement layers before choosing an implementation
| Layer | Budget unit | Granularity | Stop behavior | Observability | Bypass resistance |
|---|---|---|---|---|---|
| Orchestrator | Tokens, requests, steps, time, estimated cost | Per call, run, worker, and project | Pause, terminate, or request approval | Detailed ledger and termination code | High when all credentials and tools pass through it |
| Provider controls | Requests per minute, tokens per minute, dollars | Project or organization | Provider error, commonly HTTP 429 for a hard spend limit | Provider usage and limit dashboards | Medium; does not distinguish individual tasks |
| Network gateway | Bytes, domains, connections, downloads | Runtime, tenant, or egress identity | Block transfer, reset connection, or quarantine | Proxy and flow logs | High for browser traffic if direct egress is disabled |
| Human approval | Action count and consequence | Individual action | Hold until signed approval or deny | Approval record and actor identity | High for authorized actions, but not a substitute for usage limits |
Troubleshoot common runaway patterns
| Symptom | Likely cause | Correction |
|---|---|---|
| The agent keeps opening pages after its token budget appears full | Browser calls are not charged against the same run ledger, or reservations are non-atomic | Route every tool through the orchestrator and reserve request, step, byte, and cost allowances before dispatch |
| One search consumes most of the run | return_token_budget is unlimited or too high |
Set a finite per-search value and a cumulative search-payload ceiling |
| Costs exceed the dashboard estimate | Output, reasoning, cached-token, or retry usage was omitted | Reconcile provider-reported fields and keep reservations until asynchronous usage arrives |
| Parallel workers bypass the cap | Each worker received the full parent budget | Allocate child sub-budgets from a parent reservation and use a centralized atomic counter |
| Bandwidth spikes during apparently small tasks | Images, media, redirects, or downloads are unmetered | Measure at the egress gateway, cap response and download sizes, and enforce an allowlist |
| A run fails with HTTP 429 | Provider rate or hard spend limit was reached | Classify the error, stop unnecessary retries, inspect project-level usage, and resume only with remaining task budget |
| The agent performs an unsafe action within its allowance | Usage authorization was mistaken for action authorization | Insert a separate human-confirmation gate and use a read-only browser by default |
Deployment checklist
- Create a run record containing independent money, token, request, browser-step, byte, and time ceilings.
- Give the orchestrator the only credentials that can call models, search, and browser tools.
- Implement atomic pre-call reservations and post-call reconciliation.
- Set finite search result-token budgets; avoid
unlimitedunless the run has an explicit high-effort policy. - Apply provider RPM, TPM, and hard-spend controls at project or organization scope.
- Force browser egress through an allowlisted, byte-counting gateway.
- Cap screenshots, redirects, downloads, navigations, and total browser steps.
- Separate read-only browsing from action-enabled browsing and require confirmation for consequential actions.
- Record actual usage and one termination code for every run, including successful completion.
- Review distributions weekly and adjust only the ceiling that evidence shows is restrictive.
This layered design answers different failure modes: the orchestrator limits an individual task, provider controls absorb fleet-wide spikes, and the network boundary prevents unmetered browser traffic. Together they make an autonomous web agent predictable enough to operate without an open-ended browsing or billing loop.
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