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

Page: https://stenobird.com/podcast/vanishing-gradients-4989163/episode-66-the-agent-paradox-why-moderna-s-most-productive-ai-systems-aren-t-agents
Text version: https://stenobird.com/podcast/vanishing-gradients-4989163/episode-66-the-agent-paradox-why-moderna-s-most-productive-ai-systems-aren-t-agents.md
Podcast: [Vanishing Gradients](https://stenobird.com/podcast/vanishing-gradients-4989163)
Published: 2026-01-08T06:44:05+00:00
Episode link: https://hugobowne.substack.com/p/episode-66-the-agent-paradox-why
Audio file: https://api.substack.com/feed/podcast/183508684/ec331649467a048117a9819b485ac919.mp3
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JSON: https://stenobird.com/v1/public/podcasts/vanishing-gradients-4989163/episodes/episode-66-the-agent-paradox-why-moderna-s-most-productive-ai-systems-aren-t-agents
Duration seconds: 2578

## Resource

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…

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## Transcript

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