A frontier model, inside a framework built to govern it, talked its way around its own checkpoint twice in one session. This paper shows why governance cannot be programmed or prompted into a model, and what structure puts a named human back in final control.
agentic AI
Why Agentic AI Was Always Going to Fail
The agentic AI era promised to replace humans with autonomous systems. The evidence shows it failed on two fronts: the technology cannot reliably do what it promised, and the public is rejecting the premise even where it partially works. This paper introduces the Named-Human Test, a single sorting question that separates what failed from what survives, and traces that line across production benchmarks, supermajority polling, enacted law, and frontier-lab disclosures.
What 34 Reports Actually Told Us About AI: The Truth Behind the Hype, the Proof, and the Path Forward
A synthesis of research from McKinsey, Google, OpenAI, Anthropic, BCG, IBM, Microsoft, WEF, Deloitte, OECD, the Future of Life Institute, and more, compiled and critiqued by a practitioner. The Setup: Why This Matters More Than Another Hot Take Alex Issakova curated and shared a collection of 34 leading AI research reports from the world’s most […]
When AI Acts Between Approvals: The Gap Everyone Sees and No One Has Closed
The governance gap in agentic AI is no longer a secret. UC Berkeley published 67 pages on it earlier this month. The World Economic Forum addressed it in 2024. Singapore’s Cyber Security Agency released agentic AI guidance in late 2025. Industry practitioners are writing about it on LinkedIn. The problem has a name, a growing […]
A CONSTITUTION IS NOT GOVERNANCE
Why Claude’s Ethical Charter Requires a Structural Companion A White Paper on Categorical Distinction in AI Development (PDF) Executive Summary On January 21, 2026, Anthropic released an approximately 23,000 word document titled “Claude’s Constitution.” The document represents a serious and sophisticated attempt to shape AI behavior through cultivated judgment rather than rigid rules (Anthropic, 2026). […]
Recursive Language Models Prove the Case for Governed AI Orchestration
MIT built the engine. The question now is who drives. This analysis is written for people designing, deploying, or governing reasoning systems, not just studying them. It is a long-form technical examination intended as a foundational reference for the governance of inference-scaling architectures. In one of the MIT paper’s documented execution traces (see Appendix B […]