Agentic AI validates itself in real time, creating audit trails that help product teams build both reliable systems and the transparency needed for legal compliance when AI decisions need explanation.
Product counsel need to become infrastructure assessors, evaluating not just model capabilities but whether entire enterprise ecosystems can handle advanced AI before deployment.
Microsoft just released their Agent Factory framework, and it's forcing a rethink of how we approach AI governance. These aren't the AI tools…
AI systems won't fit our old categories, and our legal frameworks haven't caught up yet.
The architectural shift to persistent, structured memory is happening now. Teams building these systems need to classify the memory types their agents require and define the associated governance policies upfront.
The most practical insight involves accepting that perfect solutions do not yet exist. Traditional agency mechanisms provide valuable frameworks for identifying problems and structuring solutions; however, new technical and legal infrastructure must evolve in tandem with the technology.
Ruth Porat's human-in-the-loop mandate gives product teams a concrete framework for building agentic systems that users will trust and regulators will accept.
After Stanford Law's agentic AI program, it was clear: companies are building autonomous capabilities faster than they can deploy them responsibly. This is part of a series exploring organizational frameworks that can keep pace with AI autonomy, which emerged from that program.
