When agents forget purpose, governance has a context problem
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.
Context beats model: the AI governance shift product counsel is missing
Better context beats a better model — which means AI risk governance needs to shift from the model layer to the retrieval layer. That's where defensibility lives now.
LLMs create a new blind spot in observability
LLMs break traditional observability — and that creates a compliance gap most governance teams haven't addressed yet. If you can't trace the full AI pipeline, you can't audit it.
Treat Your AI Agent Like a Junior Associate—Not Like Magic
You wouldn't tell a first-year associate "do law" and expect good results. So why are attorneys doing exactly that with AI agents? Dan…
AI reasoning explanations fail four times in five: what to verify before shipping
The trajectory is encouraging — the most capable models performed best. But 20 percent is not a foundation for compliance frameworks.
Without observability, AI fails in silence
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 just became a security gap
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.
Why AI agents work in demos but fail in production
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.
