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.
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.
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.
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.
