Ever wonder why some teams intentionally break their own systems? Welcome to the world of chaos engineering — a practice that's not just for Netflix-scale infrastructure, but for any team that wants to build resilient, reliable applications.In this session, we'll demystify chaos engineering and explain why intentionally breaking things is actually the smart move. You'll learn:What chaos engineering really is (in plain English, no buzzwords)Why waiting for production failures is a terrible strategyHow to start experimenting with controlled failure locally, before it happens in the wildReal-world examples of chaos experiments that catch bugs you'd never find in traditional testingTools and techniques to get started without blowing up your infrastructureThrough practical demos using LocalStack's cloud emulation and chaos engineering tools, we'll simulate failures like network latency, service outages, and resource exhaustion right from your laptop.If you've ever said "it worked on my machine" only to watch it crash in production, this talk is for you—let's break things intentionally so they don't break unexpectedly.

Recreating the unique state of a cloud app environment is notoriously complex, making it difficult to diagnose bugs, collaborate with teammates, or even just pick up where you left off. Join us as we deep dive into LocalStack’s powerful state management and persistence features, exploring how to maintain, snapshot, and seamlessly share your local cloud environments using core features like persistence, Cloud Pods, and state files. Learn how to restore a previous state, share preconfigured environments with colleagues for collaboration or onboarding, or restore preset services and data for functional tests in CI. We'll also showcase LocalStack's new Model Context Protocol (MCP) integration, revealing how AI can manage, inspect, and automate your local cloud state.

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?