Surprise. We don’t have agents. I actually went in and did an audit of all the LLM applications that we’ve developed internally. And if you were to take Anthropic’s definition of workflow versus agent, we don’t have agents. I would not classify any of our applications as agents. x

Eric Ma, who leads Research Data Science in the Data Science and AI group at Moderna, joins Hugo on moving past the hype of autonomous agents to build reliable, high-value workflows.

We discuss:

* Reliable Workflows: Prioritize rigid workflows over dynamic AI agents to ensure reliability and minimize stochasticity in production environments;

* Permission Mapping: The true challenge in regulated environments is security, specifically mapping permissions across source documents, vector stores, and model weights;

* Trace Log Risk: LLM execution traces pose a regulatory risk, inadvertently leaking restricted data like trade secrets or personal information;

* High-Value Data Work: LLMs excel at transforming archived documents and freeform forms into required formats, offloading significant “janitorial” work from scientists;

* “Non-LLM” First: Solve problems with simpler tools like Python or ML models before LLMs to ensure robustness and eliminate generative AI stochasticity;

* Contextual Evaluation: Tailor evaluation rigor to consequences; low-stakes tools can be “vibe-checked,” while patient safety outputs demand exhaustive error characterization;

* Serverless Biotech Backbone: Serverless infrastructure like Modal and reactive notebooks such as Marimo empowers biotech data scientists for rapid deployment without heavy infrastructure overhead.

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 more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2206. Here is a 35% discount code for readers. 👈

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch

👉 Eric & Hugo have a free upcoming livestream workshop: Building Tools for Thinking with AI (register to join live or get the recording afterwards) 👈

Show notes

* Eric’s website

* Eric Ma on LinkedIn

* Eric’s blog

* Eric’s data science newsletter

* Building Effective AI Agents by the Anthropic team

* Wow, Marimo from Eric’s blog

* Wow, Modal from Eric’s blog

* Upcoming Events on Luma

* Watch the podcast video on YouTube

* Join the final cohort of our Building AI Applications course in Q1, 2026 (35% off for listeners)

Timestamps

00:00 Defining Agents and Workflows

02:04 Challenges in Regulated Environments

04:24 Eric Ma's Role at Moderna, Leading Research Data Science in the Data Science and AI Group

12:37 Document Reformatting and Automation

15:42 Data Security and Permission Mapping

20:05 Choosing the Right Model for Production

20:41 Evaluating Model Changes with Benchmarks

23:10 Vibe-Based Evaluation vs. Formal Testing

27:22 Security and Fine-Tuning in LLMs

28:45 Challenges and Future of Fine-Tuning

34:00 Security Layers and Information Leakage

37:48 Wrap-Up and Final Remarks

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2026. Here is a 35% discount code for readers. 👈

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Episode 66: The Agent Paradox - Why Moderna's Most Productive AI Systems Aren't Agents

Vanishing Gradients

Episode 66: The Agent Paradox - Why Moderna's Most Productive AI Systems Aren't Agents