Reliable AI starts with the failure path
A practical checklist for making AI workflows reliable with durable state, bounded retries, evaluation, tracing, and human review.
An AI feature is not ready because one prompt returned a good answer.
Before adding another model or prompt, I want five things in place:
- A durable workflow state: received, processing, succeeded, retryable
failure, or permanent failure. - Bounded retries with timeouts, backoff, idempotency, and a dead-letter path.
- A small evaluation set that checks correctness, safety, structure, cost,
and latency. - A trace across the request, queue, retrieval, model, tools, and database.
- A clear human path for security warnings, unclear input, or possible unsafe
output.
This changes how I think about AWS architecture. The model call may be the most
visible part, but the queue, state store, observability, IAM boundaries, and
deployment pipeline decide whether people can trust the product.
visible part, but the queue, state store, observability, IAM boundaries, and
deployment pipeline decide whether people can trust the product.
I am applying this approach to public work in Go, Python, TypeScript, AWS, and
Kubernetes. The first note is here:
https://oduonye.com/blog/posts/reliable-ai-starts-where-the-demo-ends/
Kubernetes. The first note is here:
https://oduonye.com/blog/posts/reliable-ai-starts-where-the-demo-ends/
What failure mode has caused the most trouble in an AI workflow you have run?
Enjoyed reading this content? Let the author know!
Your likes, comments, shares, and saves help creators reach more builders.
Loading recommendations
Loading article