Error Handling and Fallbacks
Things will break. The question is whether your workflow recovers gracefully.
What You'll Learn
- Why errors aren't bugs — they're expected behavior
- The retry-fallback-alert pattern
- Designing workflows that degrade gracefully
- How AI handles uncertainty differently than traditional code
Errors Are Not Failures
APIs go down. Data arrives malformed. Rate limits get hit. Network connections drop. These aren't signs your workflow is broken — they're normal operating conditions. The difference between an amateur workflow and a production-grade one is how it handles the unexpected.
A workflow without error handling is a ticking time bomb. A workflow with error handling is a resilient system that runs for months without intervention.
Retry → Fallback → Alert
Retry: The API timed out? Wait 5 seconds, try again. Most transient errors resolve themselves. Set a retry limit — typically 3 attempts with increasing wait times (5s, 15s, 45s).
Fallback: Retries exhausted? Switch to Plan B. If the primary email service is down, route through the backup. If AI classification fails, apply a default category and flag for human review.
Alert: Fallback activated? Notify someone. Not with a panic alarm — with a clear message: what failed, when, what the fallback did, and what needs attention. Then the workflow keeps running.
When AI Isn't Sure
Traditional code either works or throws an error. AI has a third state: uncertain. An AI classifier might be 95% confident a support ticket is "billing" but only 40% confident another is "technical." Your workflow needs to handle that confidence spectrum.
Set confidence thresholds. Above 80%? Act automatically. Between 50-80%? Act but flag for review. Below 50%? Route to a human. This turns AI uncertainty from a liability into a feature — the system knows what it knows and what it doesn't.
The Show Must Go On
The best workflows don't stop when something breaks — they do the best they can with what's available. If step 3 of a 6-step pipeline fails, can steps 4-6 still run with partial data? Often, yes. Design your workflows so that each step is as independent as possible, contributing to the whole but not completely blocking it.
Think of it like a restaurant kitchen. If the dishwasher breaks, you don't close the restaurant. You adapt. Your workflows should do the same.
Knowing What Went Wrong Changes Everything
Not all errors are created equal. Categorizing errors helps you build the right response for each type:
Transient errors: Network timeouts, rate limits, temporary service outages. These resolve themselves. Strategy: retry with backoff. Most APIs recover within 30-60 seconds.
Data errors: Malformed input, missing required fields, type mismatches. These won't fix themselves on retry. Strategy: validate at the boundary, return a clear error message, route to a dead-letter queue for manual review.
Configuration errors: Expired API keys, wrong endpoint URLs, missing environment variables. These affect every request until fixed. Strategy: detect early (test on startup), alert immediately, fail fast rather than retrying endlessly.
Logic errors: The workflow ran successfully but produced the wrong result — wrong classification, wrong routing, wrong calculation. The hardest to detect because no exception is thrown. Strategy: output validation, sample auditing, and anomaly detection.
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