Amazon

Every engineering organization is adopting AI coding tools, but everyone isn't seeing the same transformative results. Amazon Stores set out to find a way to accelerate teams - increasing output by three times - through AI-native development and achieved a 4.5x median gain. The difference wasn't the tools alone; it was pairing them with new practices that changed how engineers work. In this Masterclass, Amazon Stores leadership will unpack the playbook behind those results, with a focus on spec-driven development - where structured specifications become the connective tissue between human intent and AI execution, enabling agents to reason across complex codebases instead of hallucinating. You'll explore how teams reskilled engineers to become orchestrators of AI agents rather than executors of every detail, restructured codebases and documentation for AI consumption, and drew a sharp line between disciplined AI-native development and ad-hoc "vibe coding." Underpinning this work, AWS services - including tools like Kiro - provide the development infrastructure that teams build on. We'll also candidly discuss what bottlenecks we encountered at 3x speed and how to navigate the cultural change needed to drive this transformation. Whether you're leading a startup engineering team or an enterprise organization, you'll leave with a practical framework for turning AI adoption into sustained, measurable development acceleration.

This panel brings together visionary AI startup founders who architected their companies from day one with AI at their core. We'll explore how they're designing autonomous AI systems that execute complex workflows, scaling from proof-of-concept to production-ready solutions enterprises trust and leveraging proprietary data to create competitive advantages. The discussion will address building reliable AI systems—from managing hallucinations to implementing governance frameworks—and how strategic partnerships with cloud providers like AWS enable startups to scale infrastructure and deliver enterprise-grade reliability. Key Discussion Points: Agent-First Architectures, Enterprise Adoption & Scale, Data as Differentiator, Building for Reliability,

As AI agents become more autonomous, executives face a critical trust gap when deploying them for higher-stakes applications where incorrect outputs and actions carry significant consequences. Building trustworthy agents requires responsible AI best practices that guide how agents are built and operated, addressing considerations like privacy, fairness, transparency, and governance. Automated reasoning provides techniques to validate AI responses, check properties of generated code, or enforce policies for agentic behaviors, all using mathematical logic for provable accuracy. Join this interactive session to explore practical approaches for building more trustworthy and reliable AI agents.

In a landscape of rapid AI evolution and daily headlines, senior leaders need clarity on how Agentic AI will drive real business value at scale. This session cuts through the noise to provide a practical understanding of autonomous AI agents and how they translate into measurable outcomes across the organization. We will demystify Agentic AI through the lens of value realization — focusing on how open, interoperable architectures and democratized access to trusted data unlock speed, productivity, and innovation. We will share how both management and architectural models must evolve to operationalize autonomous AI systems securely, govern them effectively, and scale impact across the organization.

Startup Reception hosted by AWS. Open to Startup companies only. Click here to RSVP.