From Agent Demos to Provable Control

AI Governance Moves from Observability to Provable Control argues that enterprise AI governance must prove authorization, accountability, and control before an agent acts. That is Principles 10, 15, and 16: build permissions and outcome audits into the workflow, not bolted-on monitoring.

Show HN: CUA-S1 – A System One Model for Computer Use combines isolated desktops, automation tools, computer-use models, and benchmarks for safer desktop agents. Outcome engineers get a concrete Principles 07, 14, and 16 stack for building, containing, and evaluating agents that operate real interfaces.

Jev Cuts AI Decision Costs 100x as Vercel and Cloudflare Adopt It reports that Jev accelerates agent tool selection and cuts decision costs while matching leading frontier models on workflow evaluations. Cheaper orchestration makes Principles 09 and 12 more practical: route tasks across specialized tools without spending the budget on deliberation.

RSA-896 documents a Claude-assisted factorization of RSA-896 and packages the computation as a verifiable artifact. It is Principles 02 and 16 in miniature: agent-produced results become useful when independent checks can establish ground truth.

Brood War Bench tests frontier agents on real-time strategy, exposing weaknesses in coordination, planning, and resource management. The benchmark turns vague claims about multi-agent capability into measurable failure modes—Principles 09, 14, and 16 for anyone designing agent teams.