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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Use retries to repeat a request that failed for a temporary, recoverable reason; use a model fallback to send the review to a different model after a specific trigger. Treat them as separate policies with separate limits. A fallback is not automatically outage failover: Anthropic’s documented fallback, for example, responds to selected safety refusals, not rate limits or server errors.
For a dependable review pipeline, classify the failure, retry only when replay is safe, honor provider delay instructions, and put a deadline around the whole operation. Switch models only for a trigger your routing policy explicitly handles, and record which model ultimately produced the review.
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How are retries different from model fallbacks?
A retry sends the request again, usually to the same model, after a retryable failure. A fallback sends a request to another model after a defined trigger, such as a refusal or an outage response. They solve different problems: a second attempt can recover from temporary trouble, while a second model can help only if it is eligible, compatible with the request, and appropriate for that trigger.
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if a retryable failure occurs and replay is safe:
wait according to Retry-After or backoff policy
retry within the shared attempt and time budget
if a configured fallback trigger occurs:
route to an eligible, compatible model
otherwise:
stop and report the failureDo not assume that a provider’s setting called “fallback” covers every kind of failure. Check its documented trigger and handling for each error class before relying on it.
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Which failures should you retry, route elsewhere, or stop on?
Inspect the response body and provider error code as well as the HTTP status. One status can represent different problems, and those problems do not all call for the same action.
| Failure or state | Default handling | What to check |
|---|---|---|
| Temporary rate limit or overload | Retry only if the provider identifies the error as temporary and the operation is still within its budget. Honor a valid Retry-After value. |
Response body and error code; server delay; remaining deadline. Unsuccessful requests can still count toward per-minute limits. OpenAI rate-limit guidance |
| Transient network failure | Retry only when the request can safely be replayed and a retry policy explicitly includes the failure. | Whether any response or streamed output has already reached the caller; cancellation state; remaining budget. OpenAI Agents SDK replay-safety guidance |
| Invalid request or configuration | Stop. Correct the request, model configuration, or integration rather than repeating the same invalid call. | Provider error details and the request features being sent. |
| Quota, billing, or another issue requiring action | Stop and surface the issue to the operator or user. Repeating the request does not resolve the underlying problem. | Error code and account or project state. OpenAI rate-limit guidance |
| Semantic refusal | Use a refusal-triggered fallback only if the provider documents it, the request is eligible, and your policy permits another model to handle it. | Refusal signal, fallback eligibility, and the target model’s request compatibility. Anthropic’s documented feature is refusal-triggered, not general outage failover. Anthropic refusal and fallback documentation |
| Streaming has begun, or the call is stateful | Do not blindly replay. Stop, preserve the partial result or report an incomplete review, and retry only if the operation’s replay-safety rules allow it. | Whether response events or output have already been consumed and whether replay could duplicate effects. OpenAI rate-limit guidance and Agents SDK model documentation |
How should you configure retry delays and limits?
- Classify before retrying. Allow only documented temporary failures into the retry path. Do not treat every 429, 5xx, timeout, or network error as interchangeable; check the provider’s error details.
- Honor a usable server delay. For eligible temporary OpenAI rate limits, the guidance says to treat a valid
Retry-Aftervalue as the minimum delay, then add a small random delay to reduce synchronized retries. If no usable hint exists, use exponential backoff with jitter. OpenAI rate-limit guidance - Do not retry sooner than the provider’s delay. If a valid server delay exceeds the maximum your service is willing or able to wait, defer the review rather than retrying early.
- Set two hard bounds. Configure both a maximum attempt count and a total elapsed retry budget. A per-attempt timeout is not a substitute for an end-to-end deadline.
- Use one shared retry budget. Official OpenAI SDKs automatically retry some eligible 429 and 503 responses, subject to SDK settings. If your application also retries, disable one layer or account for attempts at both layers in the same cap; otherwise the combined attempt count can multiply. SDK behavior around
Retry-After, especially longer delays, varies by SDK version and configuration. OpenAI rate-limit guidance - Respect cancellation and deadlines. Stop when the caller cancels or the operation deadline expires. For an agent run, account for model-call timeouts, retry delays, tool execution, and other work in the overall deadline.
- Define terminal behavior. When attempts are exhausted or a failure is not retryable, return a clear failed or deferred review state. Do not silently treat an incomplete response as a completed review.
There is no universally correct retry count or wait schedule established by these provider documents. Set them from your service’s latency budget and workload, then validate that the resulting behavior stays within rate limits and deadlines.
How do you configure retries in the OpenAI Agents SDK?
The OpenAI Agents SDK for Python documents retries as opt-in for general model calls: configure ModelSettings(retry=...) and a ModelRetrySettings policy. Its example exposes a maximum retry count and exponential-backoff settings such as initial and maximum delay, multiplier, and jitter, and shows composing policy for provider advice, Retry-After, network failures, and selected HTTP statuses. The exact API is version-sensitive; check the documentation matching the SDK version installed in your project before copying or adapting the example. OpenAI Agents SDK model documentation
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Keep per-call and operation-level limits distinct. A model-call timeout bounds one attempt, including transport waits; a later retry may receive its own timeout, and that timeout does not bound the full agent run, tool execution, or time spent waiting between attempts. Set an overall operation deadline as well. The SDK’s replay-safety rules also matter: aborts and unsafe streamed runs are not retried, and response events reaching the caller prevent replay. OpenAI Agents SDK model documentation
When should an AI code review switch to a fallback model?
Choose the trigger deliberately. A refusal, temporary rate limit, provider outage, and malformed request are distinct events. A policy intended for one should not be assumed to cover the others. If you need outage failover, implement and test an explicit client- or gateway-level policy for the relevant error classes rather than relying on a refusal fallback.
Anthropic’s documented refusal fallback
Anthropic documents a beta server-side refusal fallback using fallbacks="default" with the server-side-fallback-2026-07-01 beta header, or an explicit ordered list of up to three fallback models. The models must be distinct, permitted targets, and able to accept the request’s features; the API validates compatibility up front. The documentation also describes SDK middleware and manual retries. These are Anthropic-specific settings, and the beta header, request shape, and available targets should be checked against the current documentation before use. Anthropic refusal and fallback documentation
This fallback is triggered by a classifier refusal, identified by stop_reason: "refusal". Anthropic says rate limits, overload, and server errors for the requested model are returned as-is. A fallback attempt may itself be rate-limited or overloaded, so it does not guarantee a successful review. Anthropic refusal and fallback documentation
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsHow do you make sure the fallback can handle the same review request?
Before routing, confirm that the target is accessible to the project and can accept the request as constructed. A model that is available in a catalog may still be unavailable under a particular plan, product surface, policy, or supported version.
- Check context and output limits against the review payload and expected findings.
- Check support for tools, structured output, reasoning settings, streaming, and any stateful conversation requirements used by the primary request.
- Confirm the target is permitted by account policy and accessible through the API surface you use.
- Keep model identifiers and fallback order under intentional change control. Recheck access, model changes, and retirement information when releasing or maintaining the pipeline.
Availability can vary by plan, surface, policy, and supported version, and model catalogs and retirement schedules change. GitHub’s Copilot model documentation illustrates why a configured list should be revalidated rather than treated as permanent; its availability details apply to Copilot, not automatically to other providers. GitHub Copilot supported models
How do you know which model actually reviewed the code?
Log routing at the attempt level, not just the final status. Preserve enough metadata to explain how the review was produced and to diagnose incomplete or unexpectedly routed calls.
- Request or review identifier and timestamp.
- Configured model, actual response model, and attempt number.
- Failure class or fallback trigger, including provider error code where available.
- Retry delay and whether it came from a server hint or local backoff policy.
- Whether output or response events had already been received before a failure.
- Terminal error, completion status, and review disposition.
For Anthropic’s documented fallback, the top-level response model identifies the serving model and usage.iterations records attempts. Capture those fields where available alongside your own attempt metadata. Anthropic refusal and fallback documentation
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How should you validate the review policy before relying on it?
Exercise the routing and retry behavior with representative code changes and controlled failure cases. Measure whether findings remain useful when a different model handles the request; do not assume a fallback model is equally accurate for your codebase or review task.
- Test eligible temporary failures, non-retryable account or request errors, and fallback triggers separately.
- Verify the server-delay path, jitter, maximum attempts, overall deadline, cancellation, and terminal status.
- Test a stream failure both before and after output begins; confirm that partial output is not silently replayed or reported as complete.
- Check logs and alerts for the actual responding model, trigger, attempt count, delays, and final error.
- Review false positives and false negatives against representative changes, and require human validation before incorporating findings or suggested code into production.
GitHub advises careful validation and thorough human review, including security review, before incorporating model suggestions into production. Recheck provider documentation and model availability at release time because settings and inventories can change. GitHub Copilot supported models
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