In November 2025, we demonstrated through systematic cross-vendor testing that autonomous agentic AI, as marketed, does not exist in any major commercial LLM system. We introduced Evans’ Law for Agentic AI (predicting collapse at three discrete operational thresholds), the Evans Ratio (E = Cp/Cd, quantifying the balance between probabilistic intelligence and deterministic control), and the Brock Threshold (E = 1, the boundary between automation and agency). Three months later, the industry has confirmed this diagnosis through its own architectural choices. Rather than building systems that cross the Brock Threshold, every major AI company (Anthropic, Google, OpenAI, Microsoft, Stripe) has independently converged on designs that systematically reduce the model’s operational authority. This convergence has taken two parallel forms: atomization (decomposing agency into separable, governed components) and protocol standardization (encoding that decomposition as industry infrastructure). This paper documents both responses, introduces Operational Consequentiality (Oc), a framework for measuring what agents do, not just what they are made of, and establishes the Consequentiality Constraint (E_safe ≤ k/Oc), which formalizes the inverse relationship between an agent’s real-world impact and the probabilistic authority it can safely exercise. The central finding: the industry is not racing toward autonomous agents. It is racing toward the lowest possible model authority that enables the highest possible operational consequence. Reliability increases as agency decreases. The field is stabilizing by disassembling the very concept it markets