LocalStack Chaos API enables you to simulate outages in any AWS region or service. Chaos API provides an easy way to implement chaos engineering experiments to test a wide variety of simulated outages and failures within your application safely, without impacting your production users.Common examples can include:- Region-wide outages- DNS failovers- Service failures- Network faultsAll the testing scenarios described above can be executed within LocalStack, providing thorough coverage for critical situations in a matter of minutes rather than hours or days.In this presentation by Viren Nadkarni, we explore how Chaos API is leveraged to perform service failures in a local environment while using robust error handling to address and mitigate such issues.## Resources- Documentation: https://docs.localstack.cloud/user-guide/chaos-engineering/chaos-api/- Get access: https://www.localstack.cloud/contact

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?