2025-11-05 –, Eng
Everyone is excited about conversational AI. Everyone is implementing their own chatbots, until they have to make a conversation behave in production.
Replacing a rigid, quiz-style signup with a dynamic onboarding chat sounds great, but executing it is far from simple. It requires designing a conversation that adapts dynamically, collects data, run actions, and completes all of this within a reasonable timeframe. The real headache isn’t “adding an LLM”; it’s the engineering of an agent that can make decisions, and acts on them using automatic tool triggering, which includes presenting actual assets during the conversation.
In this talk, I’ll show how we treated onboarding as a conversation-engineering problem and shipped a production agent using our internal Python-based Agents SDK. We'll walk through the core building blocks and key considerations of creating and maintaining a real-world onboarding agent in production. By the end of the talk you will learn how to structure conversation flows that adapt dynamically, and how to engineer your agent to reliably achieve specific conversational goals.
Everyone is excited about conversational AI. Everyone is implementing their own chatbots, until they have to make a conversation behave in production.
Replacing a rigid, quiz-style signup with a dynamic onboarding chat sounds great, but executing it is far from simple. It requires designing a conversation that adapts dynamically, collects data, run actions, and completes all of this within a reasonable timeframe. The real headache isn’t “adding an LLM”; it’s the engineering of an agent that can make decisions, and acts on them using automatic tool triggering, which includes presenting actual assets during the conversation.
In this talk, I’ll show how we treated onboarding as a conversation-engineering problem and shipped a production agent using our internal Python-based Agents SDK. We'll walk through the core building blocks and key considerations of creating and maintaining a real-world onboarding agent in production. By the end of the talk you will learn how to structure conversation flows that adapt dynamically, and how to engineer your agent to reliably achieve specific conversational goals.
No previous knowledge expected
Noa Radin is a Data Scientist at HoneyBook, where she works on improving user onboarding and product experience. Prior to that, she led a data science team at ThetaRay, designing anomaly detection solutions for global banks and fintechs. Noa holds an M.Sc. in Data Science and Engineering from Ben-Gurion University. In her free time, Noa enjoys hiking and relaxing at the beach.