Enterprises deploying AI components need more than metrics. They need reliable systems that developers can trust and iterate on quickly. In this talk, I will share how to design product evaluation pipelines tailored for AI native development workflows. We will explore how to utilize LLM as a judgment technique, combined with human-in-the-loop feedback, to assess and refine generated outputs in a manner that is both repeatable and efficient for engineering teams.

The session will cover how to transform evaluation into a developer-first workflow, including patterns for parallel evaluation runs, reliability tracking, and regression alerts. We will also discuss how to maintain compliance and audibility in industries where trust and oversight are crucial.

As a forward look, I will share how these evaluation practices build the foundation for agentic systems that improve themselves and how they prepare teams for future advancements and demanding customer needs.

Anil is an award-winning senior product, tech, and AI leader with over 15 years of experience managing large-scale enterprise platforms and consumer-grade products. He has a proven track record in