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When a model-based classifier is unavailable, your application still needs a predictable way to decide whether to retry, wait, or stop. Make a deterministic failure map the last dependable layer: classify known errors from their types or normalized categories, attach bounded actions, and route unknown cases to an explicit default. Use a model to help classify failures only when the added interpretation is worth another call—and never make that call the sole fallback for its own outage.
Build the fallback around a failure taxonomy
Start by grouping failures according to what they mean for the operation, not just by the text of an exception. A useful taxonomy distinguishes transient conditions from errors a retry cannot fix. Apache Airflow’s retry-policy examples include rate limits, network errors, transient failures, authentication failures, invalid data, missing resources, and permanent errors. Give each category a concise definition so neighboring cases are less likely to be confused.
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Then map each category to an action and, where relevant, a delay. Airflow’s example configuration retries rate limits after 60 seconds, network errors after 10 seconds, and transient failures after 30 seconds; it fails authentication, invalid-data, missing-resource, and permanent-error categories. Those are examples in Airflow provider documentation version 0.10.0, not universal operating values. Your own delay and action should reflect the provider’s guidance, the operation’s safety, and the cost of waiting.
Separate deciding what happened from deciding what to do
A useful policy has two distinct parts: a classifier chooses a category from a finite set, and your configured mapping decides the action for that category. Keeping the action in code or configuration makes the policy auditable: a model may help interpret an unfamiliar error, but it does not get to invent retry behavior.
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Airflow’s ClassifierRetryPolicy uses this pattern. Its classifier selects among configured categories, while the category table determines whether to retry or fail and can specify a delay and confidence threshold. A simpler system can skip the model entirely and map known exception types or normalized error categories directly to actions.
Choose the right retry boundary
Retry only failures that may clear without changing the request. The current Gemini API troubleshooting guidance gives 429 and 503 as examples of retryable errors, recommends exponential backoff with jitter and a maximum retry count, and warns against retrying client errors such as 400, 402, and 403. These examples describe Gemini; do not assume another provider assigns identical meanings to the same status codes. Check the provider’s current error documentation.
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Retries should be bounded. Exponential backoff spaces repeated attempts progressively farther apart; jitter varies the waits so many clients are less likely to retry in lockstep. Set a maximum number of attempts and decide what happens when it is reached. For context, Google says the Gemini Python SDK automatically retries transient errors up to four times, with an initial delay of approximately one second and a maximum delay of 60 seconds. Those are SDK-specific documented defaults, not a universal setting for applications.
Define behavior when classification itself fails
The classifier request can time out, fail authentication, hit a provider outage, or return an unusable response. In each case, apply the deterministic mapping directly rather than asking the unavailable classifier to interpret its own failure. Airflow documents that its policy falls back to configured rules when a model call fails, or to the task’s standard retry behavior if no fallback rules are configured.
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Make the unmatched-error behavior explicit too. A conservative default might defer to the task’s standard retry policy or fail visibly for operator review. Choose based on whether repeating the operation is safe and whether delaying or dropping the work is more costly. Avoid a catch-all that retries indefinitely or silently treats every unknown error as transient.
Bound the classification call separately from the task retries. Airflow’s current API reference documents a 30-second default timeout for its model-backed retry policy; confirm the version deployed before relying on that value. A classifier timeout limits how long one decision can take, while a retry cap limits repeated task work. They solve different problems.
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Use confidence thresholds carefully
A threshold can prevent a reachable but uncertain classifier from controlling the decision. Airflow documents that a below-threshold result is discarded and the policy proceeds to a fallback policy, fallback rules, or task defaults.
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Choose the simplest policy that meets the need
| Approach | Decision dependency | Flexibility | Operational trade-off |
|---|---|---|---|
| Deterministic exception rules | Does not require a model to be available. | Constrained to the errors and categories you define. | Direct action mapping is easy to inspect; unfamiliar errors need an explicit default. |
| Model-backed classifier | Requires a separate model request to classify the error. | Can interpret varied errors within a finite category set. | Adds a call that can time out or fail, so it needs its own fallback. |
| Layered policy | May depend on a classifier or reasoning layer before reaching deterministic rules. | Can route uncertain or unavailable cases through additional decision logic. | Offers more interpretation paths, but each added layer brings latency and another availability dependency. |
Airflow documents a chain that can combine a classifier, an optional reasoning policy, and deterministic fallback rules. Use that richer chain only when its added classification value justifies the extra model request and its failure modes. For many integrations, deterministic rules alone are the clearest and most dependable baseline.
Make fallback decisions observable
Record enough structured information to explain why the policy retried or stopped. Airflow’s example logs category, confidence, threshold, action, and delay, and records retry reasons. A practical record can also include whether the classifier was unavailable or below threshold and the relevant attempt number.
Keep logs useful without exposing sensitive data. Exception strings can contain connection strings, credential fragments, or personal information. Airflow warns that its default masking applies to registered secrets and is not general-purpose PII detection; review what your application sends to logs or an external model rather than assuming masking removes every sensitive value.
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