10:30 - 11:15

As organizations scale AI agent development, robust enterprise architecture patterns become essential. This session explores proven best practices for building production-ready AI agents using Amazon Bedrock AgentCore and its core components.

Learn how to work backwards from business problems and start with focused use cases. Discover practical strategies for secure runtime deployment, standardized tool integration, evaluation frameworks, and end-to-end monitoring.

Explore agents collaboration, shared memory, identity-based access controls, and integrated observability. Walk away with actionable strategies for scaling AI agents across your organization through proper governance and team structures.

Generative AI prototypes are easy to build—but making them enterprise-ready is not. In this session, we will explore how LangFlow, a visual orchestration framework built on LangChain, can be integrated with AWS-native services to design scalable, secure, and production-grade GenAI workflows.

We will cover how to deploy LangFlow on AWS (ECS/Fargate or EKS), integrate Amazon Bedrock as the LLM provider, use Amazon S3 for document ingestion, and apply IAM, networking, and secrets management best practices. The session will also discuss architectural patterns for extensibility, observability, and governance—key concerns for real-world enterprise adoption.

Attendees will leave with a clear understanding of where LangFlow fits in the AWS GenAI ecosystem and how to move beyond PoCs toward production.

We all love Lambda - however, developers often overuse it.

In this session and true event-driven fashion, we will explore functionless architecture - connecting AWS services directly through native integrations rather than Lambda and SDK.

Eliminating the need to bundle and deploy these functions, as well as saving on cost. During the demo, you’ll see a functionless application build with AWS CDK, its direct integration to various AWS Services such as API Gateway, EventBridge, S3, DynamoDB, Step Functions, handling business logic and data flow.

Most cloud security teams have solved visibility. You can detect misconfigurations, flag drift, and generate findings all day long. But then what? The real problem was never seeing the issues, it was fixing them at scale. In this talk, We’ll share how we went from drowning in findings to setting up our own auto-remediation.

We’ll cover how to implement remediation workflows using AWS services like Security Hub, EventBridge, Lambda, and Config Rules, review open source alternatives worth considering, and explain why we ultimately chose full customization to handle our specific needs.

Whether you’re just starting to think about auto-remediation or already sitting on a backlog of findings you can’t act on, you’ll walk away with practical insights.