For one-off tasks, AWS Lambda really can be incredibly easy. You write a few lines of code, deploy it, and you have a function running in the cloud ready to respond to events, scale automatically, and that only costs you pennies.But as your application grows, so does some necessary complexity. When a few one-off functions become a full serverless backend architecture made up of interconnected services, you’ll need to pay careful attention to best practices to ensure that your application is easy to debug, maintain, and scale.That’s where AWS Powertools for Lambda fits in. It’s a suite of reusable utilities designed to simplify bringing best practices around things like logging, tracing, metrics, idempotency and more to your Lambda functions with minimal effort.This demo session will dive into some of the functionality provided by the AWS Powertools (TypeScript) core libraries, such as:Encapsulating best practices into reusable libraries for structured logging, metrics collection, idempotency, and more.Leveraging Middy middleware to integrate common cross-cutting concerns, such as injecting Lambda context or automatically flushing metric.Enabling local testing with LocalStack, allowing you to deploy and debug Lambda functions with structured logs, trace data, and embedded metrics.Providing modular examples that can be deployed to AWS or LocalStack with ease, enabling developers to explore libraries.

An agent will write you a CDK stack, a Terraform module, or a stack of IAM policies in seconds.
Whether any of it works is a separate question, and the usual way to find out is to deploy to a real AWS account and watch what breaks.
In an agentic workflow, that means giving AI access to a public cloud account, racking up costs on the AWS bill, and waiting for provisioning to complete every time you push new code to the environment.

The rise of agentic AI in the software delivery lifecycle creates a dilemma with high-stakes implications.
As agents create new applications at an unprecedented rate, how do you integrate security without slowing down delivery?

You've been there: Lambda triggers, SQS messages fly, Step Functions execute, and somewhere in the middle, something breaks. You have no idea what triggered what, what payload was passed, or where it all went wrong.
That's the black box problem of AWS development.
Once your architecture grows beyond a single service, visibility disappears fast. You're left stitching together scattered logs and redeploying just to see what's going on.
App Inspector is LocalStack's built-in observability layer that opens up that black box. It gives you a real-time, unified view of every service interaction happening inside your local cloud: what triggered what, with what payload, in what order.
In this talk, we'll walk through what App Inspector is, how it fits into your LocalStack workflow, and how to use it to catch bugs locally before they ever reach staging or production.