AI governance isn't abstract—it's decisions under constraints. Foundations covers what matters: tech concepts vital to governance (yes, we geek out here), how obligations work in practice, what privacy means for product design, and why frameworks taking shape now determine what you can build next.
MCP servers let AI agents access your APIs without custom code. Most weren't built for production security. That gap between "works in demo" and "safe at scale" is where the liability lives.
When long-running AI agents summarize their own context to stay within token limits, they're deciding what to forget. That's not an engineering problem — it's a governance one.
SaiKrishna Koorapati's piece in VentureBeat makes the case that observable AI isn't about adding monitoring dashboards. It's about audit trails that connect every AI decision back to its prompt, policy, and outcome
The accountability gap doesn't just create compliance risk. It creates operational security risk. When model developers point to deployers and deployers point to model developers, the space between them becomes the attack surface.
A new research paper from Stanford, Harvard, UC Berkeley, and Caltech — "Adaptation of Agentic AI" — provides the clearest framework I've seen for diagnosing what goes wrong when agentic AI systems move from controlled demonstrations to real-world deployment.
Advancing AI Negotiations: New Theory and Evidence from a Large-Scale Autonomous Negotiations Competition Authors: Michelle Vaccaro, Michael Caoson, Harang Ju, Sinan Aral, and Jared R. Curhan
You can't eliminate non-determinism in LLMs, and you shouldn't try. The goal is management, not elimination.
Most teams building agentic AI are trying to make LLMs do something they're fundamentally bad at: making consistent, defensible business decisions. Decision platforms were built for this exact problem - and most teams don't know they exist.
