Block and GSK show successful AI agents adapt to existing workflows rather than forcing teams to rebuild around technology. The key is making AI feel invisible while amplifying human expertise.
Your data architecture is your governance architecture—build accordingly.
Companies that succeed with AI agents aren't just automating tasks—they're rebuilding how work gets done. The difference between adding agents to old workflows versus designing new ones around agent capabilities.
Companies throwing AI agents at problems without oversight. 72% adoption, 75% governance concerns. Smart approach: experiment messy, add controls when agents need to coordinate with each other.
Agent commerce conversation heavy on vision, light on working systems. DeWitt's framework offers a practical middle path—not betting on immature technology but positioning for a customer category that will transact at scale.
Privacy isn't about access control anymore when your AI agent can infer, interpret, and act on patterns you never explicitly shared. The real exposure happens in the inference layer—what the AI concludes and synthesizes based on behavioral patterns.
Box's CTO breaks down agentic security into practical categories that help legal teams think through access controls, action authorization, and manipulation detection for autonomous systems.
I track AI agent development for work, so when I clicked on Cate Hall's TED Talk thinking it might cover autonomous systems, I was caught off guar…
