For developers, architects, and ML specialists moving AI concepts into production, building a complete, agent-based product is often confusing and complex. The landscape is saturated with new AI frameworks, many of which reinvent their own solution architectures. This creates confusion about the "right way" to build, making even seemingly simple applications look overly engineered. This talk charts a pragmatic path through this complexity using solutions from Firebase.

We will tackle this challenge from two angles using Firebase AI Logic and Firebase Genkit. AI Logic enables rapid AI feature development via secure, client-side SDKs with no backend required. Genkit, in contrast, is an open-source, server-side framework for building complex, observable, and reliable AI flows.

In this session, we'll break down how to:

Build a Flexible Architecture: Show how AI Logic solves the "quick demo" problem and how Genkit solves the "reliable product" problem (RAG, agents).

Ensure Reliability and Security: Discuss the built-in approaches to security (key and API protection), testing, and monitoring that simplify maintenance.

Enable Simple Evolution: Explain when to use the client-side approach, when to use the server-side one, and how they can coexist, allowing an application to scale without technical debt.

This talk is for those who are tired of the hype and are looking for the most direct path to learning how to build production-ready AI applications, AI agents, and the right architecture for real products that just work.