A recent MIT study estimates 95% of AI projects die in the “POC-to-deploy” gap. This failure isn't just a model problem; it's a systems, strategy, and engineering problem. After burning resources on "science projects," we developed a 5-point playbook of repeatable engineering patterns and practices that inversed our failure rate. This session dives into the five specific, battle-tested patterns we now enforce for every deployment.

This is the blueprint for engineers, architects, and technical leaders responsible for shipping real AI. We'll cover:
1. Prioritizing the "Jagged Edge": AI is not magic; it has a "jagged edge" of superhuman highs and baffling lows. We'll share our fail-fast framework for surgically scoping projects, prioritizing high-impact, low-complexity problems that fit AI's strengths.
2. Context Engineering Over Model Tuning: We'll show why context engineering trumps costly fine-tuning. It’s cheaper, faster to iterate, and fundamentally more debuggable.
3. Designing for Debuggability: We'll share the capabilities we built in our architecture for building "compound AI systems." We'll show how we build modular, agentic systems where each component can be independently tested, versioned, and debugged, avoiding the monolithic "black box" nightmare.
4. Evals & Feedback Loops as a First Principle: A project without an eval framework is a project that's already failed. We'll share case studies and patterns for designing continuous evals and human-in-the-loop (HIL) feedback systems before writing a single line of model-specific code.
5. Secure AI with Modular Guardrails: How we architect for safety, security, and compliance. This covers our best practices for building scalable, independent guardrail services for input/output sanitization, content moderation, and security, treating safety as an engineering requirement, not an afterthought.

With the concrete patterns in the blueprint, you will leave start deploying robust, scalable, and maintainable AI systems.