Apollo Research documents how AI companies deploy advanced systems internally for months before public release, creating governance gaps with serious competitive and legal implications requiring new frameworks.
Companies that succeed with AI agents aren't just automating tasks—they're choosing between rebuilding workflows around agents or adapting agents to existing human patterns. The key is knowing which approach drives adoption.
University of Washington framework argues AI agent autonomy should be a deliberate design choice separate from capability, proposing five user role levels from operator to observer.
A new analysis from the Future of Privacy Forum questions assumptions about how Large Language Models handle personal data. Yeong Zee Kin, CEO of the…
Technical accuracy gets you to functional. User comprehension gets you to transformational.
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.
