Building for Reliability
Explore how to build AI features that remain reliable despite infrastructure failures like timeouts or dropped connections. Learn to differentiate failure types, implement smart retry logic with backoff, use fallbacks such as human review or cached data, and degrade gracefully during non-critical step failures. This lesson helps you design robust systems that handle real-world issues without disrupting core functionality.
The interface chapter’s retry logic handled a model returning output that didn’t match its contract. This lesson covers a completely different kind of failure. The request never gets a response at all, because of a dropped connection, a timeout, a rate limit, or a service that’s temporarily unavailable. Nothing about the model’s reasoning was wrong here; the call simply didn’t complete, and treating it like a validation failure misses what’s actually going on. In this lesson, we will cover:
Why an infrastructure failure needs different handling than a bad response
Retrying a failed call without making a struggling service worse
Falling back once retries are exhausted, rather than leaving a request hanging
Degrading gracefully when a non-critical step fails
How this shows up in real products
The three ways engineers get ...