Jennifer Moran

Explore how chaos engineering (resilience testing) principles apply to autonomous AI agent workflows in this chalk talk. Learn to stress-test agent-based systems that handle complex, multi-step tasks using AWS Fault Injection Service.

Master the latest features of Amazon Application Recovery Controller (ARC) for enhancing application resilience on AWS. Through hands-on exercises, learn to implement automated recovery workflows, test recovery plans, and monitor recovery operations at scale.

Resilience plays a pivotal role in the development of any workload, and generative AI workloads are no different. There are unique considerations when engineering generative AI workloads through a resilience lens.

There are multiple factors that impact the performance and reliability of generative AI applications. These include the choice of model, where the model is hosted, and the length and complexity of the inputs and outputs.

Understanding the business impact of downtime and deciding when to failover a workload is a crucial decision that can have financial implications. In this chalk talk, learn how to use layers of observability and system and business metrics to get a composite view into the health of your workloads.

Is your organization prepared to handle and recover from potential disasters that can impact your workloads? Whether they are at risk from natural disasters, technical failures, or human actions, it is important that your workloads are resilient and able to perform their intended functions correctly and consistently.

Is your organization prepared to handle and recover from disasters? Whether you are facing natural disasters, technical failures, or human actions, it is important that your workloads are resilient, performing their intended functions correctly and consistently.

Disaster recovery is the process of preparing for and recovering from a disaster. An event that prevents a workload or system from fulfilling its business objectives in its primary deployed location is considered a disaster.