Guests:

• SallyAnn DeLucia, Director of Product, Arize
• Jack Zhou, Staff Engineer, Arize

In this episode, we cover:

• What tracing, observability, and evals really mean in GenAI applications
• How Arize used its own platform to build Alyx, its AI agent
• The role of customer success engineers in surfacing repeatable workflows
• Why early prototyping looked like messy notebooks and hacked-together local apps
• How dogfooding shaped Alyx’s evolution and built confidence for launch
• Why evals start messy, and how Arize layered evals across tool calls, sessions, and system-level decisions
• The importance of cross-functional, boundary-spanning teams in building AI products
• What’s next for Alyx: moving from “on rails” workflows to more autonomous, agentic planning loops

Resources & Links

• Arize AI — Sign up for a free account and try Alex
• Arize Blog — Lessons learned from building AI products
• Maven AI Evals Course — The course Teresa took to learn about evals (Get 35% off with Teresa’s affiliate link)
• Cursor — The AI-powered code editor used by the Arize engineering team
• DataDog — For understanding application traces
• OpenAI GPT Models — GPT-3.5, GPT-4, and newer models used in early and current versions of Alex
• Jupyter Notebooks — A tool for combining code, data, and notes, used in Arise’s prototyping
• Axial Coding Method by Hamel Husain — A framework for analyzing data and designing evals

Chapters: 00:00 Introduction to Sally Ann and Jack 01:08 Overview of Arize.ai and Its Core Components 01:44 Deep Dive into Tracing, Observability, and Evals 03:56 Introduction to Alyx: Arize's AI Agent 04:15 The Genesis and Evolution of Alyx 08:51 Challenges and Solutions in Building Alyx 24:33 Prototyping and Early Development of Alyx 26:22 Exploring the Power of Coding Notebooks 26:51 Early Experiments with Alyx 27:59 Challenges with Real Data 29:20 Internal Testing and Dogfooding 31:55 The Importance of Evals 35:16 Developing Custom Evals 43:09 Future Plans for Alyx 47:59 How to Get Started with Alyx