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What actually stops an agent loop?
A loop becomes expensive because the agent or its tools keep doing work. The most dependable place to stop that work is before your application dispatches the next model or tool call. A prompt telling the agent to stop can be useful guidance, but it is not an enforcement boundary: it cannot guarantee that another call will not be made.
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Use several independent controls, each aimed at a different failure mode. Run-level limits stop an individual execution; loop signals catch repeated or unproductive behavior; retry policies control recovery from transient failures; provider spend limits constrain aggregate exposure; and observability helps explain what happened. No single setting detects every logical loop.
Set a finite budget for every run
Before a run starts, assign limits that your orchestration code can check before each action. The limits should reflect the workload, not a service default. A practical budget can include:
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- Agent steps or iterations: the maximum number of decision cycles, including cycles that invoke tools.
- Model-call count: an independent ceiling on calls, including retries and calls made by delegated agents.
- Elapsed time: a deadline for the whole run, not just for an individual request.
- Token use: a run-wide allowance for input and output, with reasoning tokens and cached input accounted for where reported.
- Estimated currency spend: an optional ceiling when your application can estimate or meter the relevant model and tool charges reliably enough for a guardrail.
Check the budget before dispatch, reserve or account for the planned action, and update it with actual usage when the response arrives. If you cannot know the final usage in advance, leave a margin and stop before the remaining allowance is exhausted. A per-run estimate is an application safeguard, not a provider invoice or guaranteed exact charge.
Amazon Bedrock AgentCore documents invocation controls named maxIterations, timeoutSeconds, and maxTokens. Its documentation lists defaults of 75 iterations, 3,600 seconds, and no default token budget, respectively. Those are AgentCore service defaults, not safe recommendations for every workload; choose explicit values for your own runs. The same page lists an idle-session timeout default of 900 seconds and a maximum session lifetime default of 28,800 seconds. Session limits are not substitutes for per-run limits.
Detect repetition and lack of progress
A run may stay within a call-count budget while repeatedly issuing the same ineffective action. Add application-level signals for behavior that should trigger a pause or stop:
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- Identical tool name and arguments recur without a meaningful state change.
- The same error repeats, or a tool call alternates through a short cycle of states.
- The agent continues making calls without advancing a task-specific progress measure.
- A delegated agent or subagent keeps producing work that the parent does not use.
Define what counts as progress for the task: for example, a changed record, a completed workflow stage, or a newly obtained result. A repeated-call detector should account for legitimate repetition, such as polling a job that is still running; use a finite poll count and deadline for those cases instead of treating every duplicate as a loop.
When a guardrail trips, stop or pause the run and record the specific reason. Do not silently continue or turn a budget failure into an unbounded retry. The operator should be able to distinguish a step limit, deadline, token ceiling, repeated action, and provider refusal.
Retry errors without multiplying the bill
First identify what a 429 means in the provider response. A temporary rate-limit response is different from a spend limit, an approved-usage limit, or exhausted prepaid credits. Retrying a request that is blocked by an exhausted credit balance or spend ceiling will not fix the underlying condition.
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For a retryable rate-limit response, follow the provider’s Retry-After value when present. If it is absent, use exponential backoff with jitter. In either case, cap both the number of retries and total retry time. OpenAI’s rate-limit guidance notes that unsuccessful requests can contribute to rate limits, so immediate repeated retries can worsen the situation.
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Use provider spend limits as a backstop
Provider controls are valuable for limiting aggregate exposure, but they operate at a different scope and timing from an orchestrator’s pre-dispatch run budget.
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| Control | Scope and ceiling | What happens at the limit | Important caveat |
|---|---|---|---|
| Application run guardrail | One run; can cap steps, calls, elapsed time, tokens, or estimated currency spend. | Your orchestrator can reject the next action, then stop or pause the run. | Only works if every relevant call path checks the guardrail, including retries and delegated work. |
| OpenAI project or organization spend limit | Aggregate provider spend at the configured project or organization scope; OpenAI documents monthly spend alerts and configurable limits. | At a hard limit, affected requests return a 429 with an organization- or project-spend-limit error code. | Enforcement is not instantaneous; recorded spend can slightly exceed the configured limit while state propagates. Both organization and project limits can apply to a request. |
| OpenAI spend alert | Notification threshold for spend. | Sends an alert; it does not stop requests. | Set alerts below the hard limit if you need time to investigate, but do not treat them as enforcement. |
| Anthropic organization or workspace spend limit | Aggregate spend at the applicable organization or workspace scope, alongside request and token rate limits. | When a provider spend cap is reached, API usage pauses until reset unless a limit is raised sooner. | Tier values and limits can change; check the current account console and documentation rather than relying on a stale figure. |
Keep workloads in separate projects or workspaces where available, so one noisy agent is less likely to consume another workload’s allowance. An organization-level ceiling can provide an additional boundary, but consider the operational impact: when it blocks requests, unrelated production workloads may also be affected. Provider rate limits constrain throughput; spend caps constrain aggregate cost. Neither establishes whether an agent’s actions are useful.
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Do not estimate run cost from the visible final answer alone. Track each model call and tool action, including retries, delegated agents, and relevant third-party services. Depending on the platform and task, usage or charges can involve input tokens, cached input, output tokens, reasoning tokens, tool or sandbox activity, cache writes, and third-party costs.
OpenAI states that reasoning tokens are billed as output tokens and advises accounting for subagent use and applicable tool or third-party charges. Cached input remains billed; a high cached-input share does not by itself establish how much the whole task saved. Usage fields in Agents API observability may be best-effort or null when the value is unknown, so do not interpret a missing field as zero usage.
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If you use a currency guardrail, document which charge categories it estimates and which it cannot see. A run-level estimate can still prevent uncontrolled continuation, but it should not be presented as an exact bill when it excludes costs or depends on incomplete usage data.
Log enough to explain a stop or spike
For each run, capture a stable run or session ID, model, timestamp, call and tool sequence, reported token usage, retries, subagent activity, duration, and stop reason. Preserve relevant error codes and retry timing so an operator can tell a transient rate limit from a provider spend block.
Use traces to reconstruct activity, not as the sole source of billed cost. Amazon Web Services says AgentCore traces and logs help identify calls and operations, but directs customers to Cost Explorer or the Cost and Usage Report for billed usage. Reconcile trace identifiers and time ranges with provider billing records when investigating an incident.
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Roll out the safeguards in a controlled order
- Inventory every call path. Include top-level model calls, tools that invoke models, retries, and subagents. Identify the component that can block each next dispatch.
- Choose run budgets. Set explicit step, call-count, elapsed-time, and token limits for each workload. Add an estimated currency guardrail only if you can define what it measures.
- Add progress and retry rules. Define repetition signals, finite retry counts and deadlines, idempotency behavior, and handling for provider retry headers.
- Configure provider backstops. Set project or workspace limits where available, then an appropriate organization ceiling. Place notification thresholds below hard limits if operational response time matters.
- Record activity and reconcile cost. Emit run-level traces and usage metadata, then compare them with provider billing data rather than assuming a trace is a bill.
- Exercise failure paths safely. In a controlled environment, simulate repeated tool errors, a stuck state transition, nested delegation, and budget exhaustion. Confirm the next call is blocked and the stop reason is visible to an operator.
How to interpret the $2,000 scenario
The $2,000 figure in the headline is an illustrative incident scenario, not a documented typical loss or measured frequency of runaway-agent events. Official platform documentation establishes configuration options and provider-specific limits, but it does not establish how often logical loops occur or what they usually cost. Use your own workload traces and billing records to estimate exposure.
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