Episode 119: Rewiring Your Data Science Thinking for the Agentic AI Era

The shift to agentic AI doesn't make data science skills obsolete. But it does require data scientists to rewire how they think about familiar concepts, such as uncertainty, model evaluation and accountability, in their work.

In this episode, Jia Huang joins Dr Genevieve Hayes to explore what that rewiring actually looks like, and why data scientists are better placed than almost any other profession to make it.

You'll discover:

1. Why data scientists are better prepared for the agentic AI era than they might think [03:00]
2. How the data scientist's role is shifting from analyst to system designer [06:37]
3. The three types of uncertainty in agentic AI systems [11:58]
4. Why context engineering is the new feature engineering [23:19]

Guest Bio

Jia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems.

Links

• Connect with Jia on LinkedIn
• Follow Jia on Substack
• Agent Design Pattern Society (ADPS) website
• Jia's AI agent design position paper
• Connect with Genevieve on LinkedIn
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