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

About this episode

Hugo Bowne-Anderson and Eric Ma discuss the limitations of AI agents in favor of reliable workflows in data science applications at Moderna.

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…

People in this episode

Host: Hugo Bowne-Anderson

Guest: Eric Ma

Topics covered

• AI systems
• workflow
• reliability
• data science
• biotech
• LLMs

Keywords

• agents
• workflows
• LLMs
• data science
• biotech
• regulatory risk
• permission mapping

Mentioned in this episode

Organizations: Moderna, Anthropic

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