Engineering with AI is no longer about proving capability. It is about ensuring reliability. While LLMs offer immense potential, their non-deterministic nature creates a significant gap between a successful PoC and an enterprise-grade application.

This track focuses on the engineering fundamentals required to bridge that gap. We move past the hype to explore the tools, techniques, and architectural patterns needed to design, build, and maintain scalable AI-native systems.

What you will learn

Production Patterns for AI Agents: Practical methods to move agents from promising automation to reliable enterprise tools.
Evaluation & Assessment Frameworks: How to measure and validate non-deterministic systems in production environments.
Scaling AI-Native Architecture: Blueprints for integrating AI into the full software lifecycle without compromising system stability.
Strategic Investment Guidance: Frameworks for technical leaders to decide when, where, and how to invest in emerging AI technologies.

Why this matters now
Roadmaps are rapidly adding AI-assisted features, but delivery velocity is often throttled by concerns over safety and reliability. This track provides the practitioner-led patterns to de-risk your AI implementation and turn experimental models into durable, production-ready systems.