Perspective on building, running and governing AI agents in the enterprise: authority, evidence, risk and the controls that let organizations delegate more.
Ask almost any team how they manage the risk of an AI agent, and the answer arrives quickly: "There's a human in the loop."
Every AI evaluation seems to start with the same number. The model is 95% accurate. The agent gets it right 97% of the time.
Say "determinism" in a meeting about AI and it can sound like nostalgia, a wish to go back to rules engines and flowcharts.
Most failures announce themselves. A service goes down, an error appears, someone gets a page at 2 a.m.
Most AI governance programs begin with an inventory. Which agents do we have, who owns them, what can they touch?
The demo went well. The business sponsor was excited, the pilot worked, and everyone agreed the platform could save the team real time.
Enterprises spent two decades removing silos. Vendor-by-vendor agents are building them again, and this time the silos can act.
AI exclusions are arriving policy by policy, at renewal. What changed, why insurers are moving, and what to expect in this year's renewal conversation.
A dashboard isn't evidence. Eight questions to put to every AI platform you rely on before your next renewal, including us.
Schedule a 45-minute architecture review. We'll map where authority, enforcement and evidence sit today across the systems your agents touch, and show you the gaps either way.