So I think we’re really at a historical moment, and the opportunity is massive. Almost 15 years ago, we were promised that data science was going to be this incredible thing and create all this value for people. And I think nowadays it’s mostly viewed as a cost center in most companies. I think we can really now fulfill that original promise with agentic data science.

Thomas Wiecki, Co-creator of PyMC and Founder at PyMC Labs, joins Hugo to talk about how agentic data science is finally fulfilling the promise of Decision Intelligence.

We Discuss:

* Decision Engines: Transform data science from a cost center providing cryptic answers into a real-time decision intelligence hub delivering actionable outcomes;

* Tame the “Garden of Forking Paths”: Overcome human shortcuts by running parallel analyses to provide an honesty check, revealing the true robustness of business conclusions;

* Multiplayer Data Science: Foster organizational learning by moving agents into team chats, democratizing “what-if” questions and reducing context-switching friction;

* The Full Agentic Data Science Stack: Beyond harness and skills, the full stack includes orchestration for parallel analyses and a causal eval layer to measure actual outcome improvement;

* Agentic Dashboards: Move beyond static BI; use chat interfaces to inquire into models and generate real-time, custom visualizations for specific follow-up questions;

* Encode Professional Judgment as Skills: Elevate agent performance by encoding expert domain standards and high-fidelity workflows into specific Agent Skills, rather than relying on LLM pre-training;

* Ground Decisions in Generative Processes: Prevent hallucinations by forcing agents to model underlying physical or behavioral processes, providing a programmatic guardrail aligned with market realities;

* Scripted Causal-Bayesian Workflows: Their methodologically structured nature—from prior elicitation to posterior predictive checks—makes Causal-Bayesian workflows inherently automatable for agents;

* Iterative Autonomy via Skills: Achieve autonomy iteratively: verify workflows with human oversight, then encode verifiable parts as skills to hand off trusted tasks;

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉Want to learn how to apply agentic engineering to the world of data science? Come build the future of Agentic Data Science with us in our upcoming course. It’s a live cohort with hands on exercises, capstones, and reusable agent skills, OSS code, and notebooks that will 10x your data science projects. Sign up here and use the code ADSVG10 for 10% off. Hit reply to enquire about group discounts.👈

LINKS

* Thomas Wiecki on LinkedIn

* PyMC Labs

* Open-Sourcing Decision Lab: Scaling AI Judgment in Data Science (PyMC Labs blog)

* Decision AI Discord

* Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results (Sage Journals)

* The Agent Harness Reading List

* Show Us Your Agent Skills (GitHub)

* Agentic Data Science course with Hugo, Thomas, and Luca (10% off with code ADSVG10)

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

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