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When a model-based classifier is unavailable, your application should not ask that same unavailable model what to do next. Use deterministic rules to map known failure types to explicit actions, and send unrecognized cases to a visible, conservative default. Keep classification separate from action: even when a model suggests a category, your own policy should decide whether to retry, wait, or fail.
Build a deterministic fallback before you need one
A model-based classifier adds a dependency to the failure-handling path. If the provider is down, the request times out, or the response cannot be used, the classifier cannot reliably decide how the original task should proceed. Define a rules-based floor that remains available independently of the model.
Start with a finite taxonomy of failures your application can recognize. For each category, specify an action and, where relevant, a delay. Apache Airflow’s retry-policy documentation provides examples including rate limits, network errors, transient failures, authentication failures, invalid data, missing resources, and permanent errors. These categories are a starting point, not a universal taxonomy; use the error semantics of your own provider and task.
- Transient and rate-limit failures: consider a bounded retry with an appropriate delay.
- Authentication, invalid-data, and permanent failures: do not retry automatically unless there is a specific reason to expect the condition to change.
- Missing resources: decide whether the resource may appear later or whether the task should fail for operator review.
- Unrecognized failures: defer to the task’s standard retry policy or fail visibly rather than silently dropping work.
The last choice depends on whether the operation is safe to repeat and what the cost is of delaying, duplicating, or losing it.
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Separate the category from the action
A classifier can propose a category; policy should own the consequence. In Airflow’s ClassifierRetryPolicy, the model selects from a finite set of categories, while the configured category table determines the retry or fail action, delay, and any confidence threshold. This arrangement constrains what a model answer can do: it cannot invent a new retry action simply by describing an error differently.
The same separation works outside Airflow. Normalize the error into a category, then look up the action in application-controlled policy. Keep the mapping reviewable and versioned so a change to retry behavior is deliberate rather than an incidental result of prompt or model changes.
Retry only errors that may recover
Retries can help with transient service conditions, but repeating a permanent error adds load and delays a decision. Google’s Gemini API troubleshooting guidance identifies HTTP 429 and 503 as examples for which retrying may be appropriate, recommends exponential backoff with jitter, and advises setting a maximum number of attempts. It cautions against retrying client errors such as 400, 402, and 403. These are Gemini-specific examples; status-code meanings and retry guidance vary by provider, so check the current documentation for the service you call.
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Exponential backoff increases the delay between successive attempts, while jitter randomizes it so clients do not all retry in lockstep. Bound the attempt count as well as the delay. Google’s troubleshooting documentation says the Gemini Python SDK automatically retries transient errors up to four times, beginning with an approximately one-second delay and capping the delay at 60 seconds. Those values describe that SDK’s behavior, not a recommended default for every application.
Also confirm that the operation is safe to retry. If repeating a request could create duplicate effects, use the provider’s idempotency mechanism where available or make the operation idempotent in your own system before enabling automatic retries.
Define what happens when classification itself fails
When a model call fails, times out, returns malformed output, or cannot authenticate, apply the deterministic mapping directly. Airflow documents a fallback to configured rules when its model call fails, or to the task’s standard retry behavior if fallback rules are not configured. Its examples illustrate one possible schedule: 60 seconds for rate limits, 10 seconds for network errors, and 30 seconds for transient failures. Those are Airflow documentation examples, not general operating values.
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If your application uses a reasoning model as an intermediate fallback, recognize that it is another model dependency and can fail too. Airflow describes a layered policy involving a classifier, an optional reasoning policy, and deterministic rules. Its classifier invocation is a separate model request, with its own availability and timeout characteristics. Use extra model-based layers only if their classification value justifies the additional dependency and latency.
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A confidence threshold can route uncertain model classifications to a fallback policy, fallback rules, or the task defaults. Airflow’s documentation describes discarding an answer that falls below the configured threshold and proceeding to fallback behavior.
Do not treat a model-reported confidence score as a calibrated probability that the category is correct. Airflow notes that the score reflects distribution concentration, not necessarily correctness; an incorrect answer can still receive a high score. Evaluate thresholds against the error cases your own service encounters, and preserve deterministic behavior for classifier failures regardless of the threshold setting.
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Bound classification time separately from retries
A classifier timeout and a retry cap control different costs. The timeout limits how long one classification decision can hold up the task. The retry cap limits how much repeated work the task can generate. Set both so a slow or unavailable classifier cannot create an unbounded wait, and so transient errors cannot cause unlimited retries.
Apache Airflow’s current API reference documents a default 30-second timeout for its model-backed retry policy and says the feature requires Airflow 3.3 or later. Treat these as Airflow-specific, version-sensitive details: verify the deployed Airflow and provider versions and their current configuration reference before adopting an example.
Make fallback decisions observable
Record enough structured information to explain why a task retried or failed. Airflow’s example logs the category, confidence, threshold, action, and delay, and records retry reasons. In another framework, equivalent fields might include:
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- Normalized failure category and selected action.
- Whether the classifier failed, timed out, returned unusable output, or fell below threshold.
- Delay and attempt number.
- A stable error identifier or sanitized provider status, where useful.
Avoid sending raw exception text to an external model or storing it indiscriminately. Airflow warns that exception strings can contain connection strings, credential fragments, or personal information. Its default masking covers registered secrets; it is not general-purpose personal-data detection. Apply your own review and redaction controls before exporting exception details.
Choose the simplest policy that meets the need
| Approach | Decision dependency | Flexibility | Operational trade-off |
|---|---|---|---|
| Deterministic exception rules | Does not require an available model. | Limited to errors and categories the rules recognize. | Actions are direct and auditable; unknown errors need an explicit default. |
| Model-backed finite-category classifier | Requires a working model call to classify. | Can map varied error descriptions to a constrained category set. | Adds latency and an availability dependency; keep configured policy in charge of actions. |
| Layered classifier, reasoning policy, and rules | Depends on one or more model calls before reaching deterministic rules. | Can add interpretation for cases the first classifier cannot confidently resolve. | More failure modes and latency; retain a deterministic final path. |
Use deterministic rules alone when the error signals are clear and predictable. Add a model classifier when it meaningfully improves category selection, but ensure a timeout or failed call reaches rules or task defaults. A more elaborate chain is justified only when its extra classification value outweighs its added request, wait, and operational complexity.
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