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.
Linux Foundation governance gives AI agents the trust infrastructure they need
Before MCP, every AI application needed custom connectors for each data source. Without foundation governance, that success creates three risks: proprietary lock-in, protocol fragmentation, or de facto control by a single company. AAIF prevents all three
When Robots Negotiate: How Human Tactics Shape AI Deals
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
AI agents fail in production because teams skip the boring parts
You can't eliminate non-determinism in LLMs, and you shouldn't try. The goal is management, not elimination.
