James Fan
Researching how to make LLM agents safe to deploy — focusing on security gaps in AI agents, such as LangGraph agents.
Previously cofounded two AI startups, led Google Cloud Speech Group, taught at Columbia University and was one of the main inventors of the IBM Watson question answering system that beat the best human contestants on Jeopardy!. Now mostly thinking about what happens when you give an AI agent access to real tools.
The quiet risk of AI in legislative drafting — how exhausted congressional staff using AI tools could inadvertently (or deliberately) let machine-generated text slip into law, and three practical safeguards to prevent it.
Human-in-the-loop as a first-class security control for LangGraph agents — why human judgment defends against novel, irreversible, and high-blast-radius actions that automation structurally cannot, covering interrupt placement, the LangGraph interrupt() pattern, sanitized review packages, approval validation against spoofing, securing the review interface, and fail-safe degradation.