This deck talks about how you can build how to get started to build intelligent agent with Strands.

Welcome to the Kiro IDE guided demo! This demo will walk you through the features and functionalities of Kiro, an integrated development environment designed to streamline your workflow.

The week after AWS re:Invent builds on the excitement and energy of the event and is a good time to learn more and understand how the recent announcements can help you solve your challenges and unlock new opportunities. As usual, we have you covered with ourtop announcements of AWS re:Invent 2025that you can learn all about here.

This deck talks about how you can build how to get started to build intelligent agent with Strands.

Welcome to the Kiro IDE guided demo! This demo will walk you through the features and functionalities of Kiro, an integrated development environment designed to streamline your workflow.

Organizations face a challenging trade-off when adapting AI models to their specific business needs: settle for generic models that produce average results, or tackle the complexity and expense of advanced model customization. Traditional approaches force a choice between poor performance with smaller models or the high costs of deploying larger model variants and managing complex infrastructure. Reinforcement fine-tuning is an advanced technique that trains models using feedback instead of massive labeled datasets, but implementing it typically requires specialized ML expertise, complicated infrastructure, and significant investment—with no guarantee of achieving the accuracy needed for specific use cases.

Modern applications increasingly require complex and long-running coordination between services, such as multi-step payment processing, AI agent orchestration, or approval processes awaiting human decisions. Building these traditionally required significant effort to implement state management, handle failures, and integrate multiple infrastructure services.

Since weannounced Amazon SageMaker AI with MLflow in June 2024, our customers have been using MLflow tracking servers to manage theirmachine learning (ML)and AI experimentation workflows. Building on this foundation, we’re continuing to evolve the MLflow experience to make experimentation even more accessible.