"Getting it right means competitive advantage; getting it wrong means expensive operational theater that impresses no one."

"For AI product development, speed bumps aren't obstacles to deployment—they're the infrastructure that makes rapid, responsible deployment possible."

AI agents are shifting from copilots to autopilots, and Noam Kolt warns their speed, opacity, and autonomy demand governance rooted in inclusivity, visibility, and liability—urgent work for product and legal teams before regulation arrives.

The intersection of AI agents and enterprise accountability fascinates me, particularly the challenge of building systems that can operate autonomously while maintaining complete audit trails and decision traceability.

That's the real tension for product teams—every safety guardrail you remove increases utility but also increases the blast radius when something slips through.

AI agents need reliability built into their architecture from day one to avoid the ongoing "reliability tax" of operational breakdowns, legal exposure, and reputational damage from unreliable autonomous systems.

Harvey's new alliance program with Stanford, UCLA, NYU, Michigan, and Notre Dame

New research gives AI agents procedural memory that learns from failures and transfers between tasks. Early results show higher success rates with lower token costs—potentially solving the economics that have held back agent adoption.