Modern software systems operate in complex, dynamic environments where failures are inevitable. Traditional monitoring and manual incident response are no longer sufficient to ensure resilience or customer satisfaction. This talk explores how to design and implement self-healing software systems by combining telemetry data with an AI-driven agentic approach. We’ll start by examining how high-quality telemetry forms the foundation for detecting anomalies and predicting failures. Next, we’ll show how modern GenAI (LLMs) can transform this telemetry into actionable insights for AI agents that interpret data, pinpoint root causes, and apply automated fixes. Through a practical, real-world example, you’ll see how telemetry and AI work together to create adaptive feedback loops that continuously improve system reliability, while freeing engineers from repetitive operational tasks.

With the growing Serverless workloads, managing and maintaining them is best recommended with Infrastructure as Code (IaC). While this holds the complete infrastructure and its configurations, we could have events from one service destined to another via configuration. When building these configurations, we could also reduce the application code making it more maintainable and scalable. In this session, Jones walked us through a fully end-to-end solution built with Amazon EventBridge and AWS Step Functions with SDK integrations which have helped him to improvise the application with just IaC and very minimal application code.

Tired of rebuilding your stack from scratch every time you run tests or restart your dev environment? In this episode, we dive into Cloud Pods, LocalStack’s powerful state management feature. You’ll learn how to snapshot your entire LocalStack environment (services, resources, data), restore that exact state across machines, teams, or CI runs, speed up test cycles and workflows, and use Cloud Pods to make testing in CI/CD faster and more reliable. Cloud Pods let you freeze your infrastructure in time. Perfect for repeatable tests, isolated dev environments, or onboarding new teammates.

dbt (Data Build Tool) helps data engineers manage data transformations using modular SQL and brings version control, testing, and documentation to their transformation logic. However, running dbt against production data warehouses like Snowflake can be slow, expensive, and risky. This session introduces a new way to develop and test dbt workflows locally using the Snowflake emulator in LocalStack. You'll learn how to set up a local dbt environment, configure dbt to connect to the Snowflake emulator, run and validate dbt models locally without using a real Snowflake account, and iterate quickly on transformations before pushing them to production. Through a hands-on factory app example, we’ll walk through how to use the Snowflake emulator to run dbt models on your laptop, helping you test logic, catch issues early, and reduce cloud costs.