A GenAI prototype can be built in an afternoon. Making it reliable enough to survive production is a very different problem.

In this session, we’ll build and break down a serverless GenAI workflow on AWS using Amazon Bedrock, AWS Lambda, Amazon API Gateway, Amazon S3, and event-driven components. Rather than focusing only on getting an LLM to return the right answer, we’ll look at the engineering problems that appear once real users, failures, latency, retries, and costs enter the picture.

We’ll explore what happens when model calls time out, downstream components fail, requests are retried unexpectedly, or an AI workflow becomes too expensive to operate at scale. We’ll then walk through practical patterns for adding failure handling, observability, asynchronous processing, retries, and cost controls without turning the architecture into unnecessary complexity.

Attendees will leave with a practical blueprint for taking a GenAI application from “the demo works” to “I’d actually deploy this.