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The benchmark you are procuring against is a dataset nobody audited Experts re-checked two widely used text-to-SQL benchmarks and found annotation errors in more than half the examples of each. Correcting the labels changed the ranking of the agents measured against them, which is the part that matters to anyone selecting a supplier on a leaderboard position. There will be no draft of the ICO's agentic AI guidance The ICO has agentic AI guidance in drafting for Winter 2026 and no public consultation on it, so no draft will circulate for anyone to argue with. What it will land on is already in print — the regulator has said that design and architecture determine how data protection law applies to an agentic system. The first AI Act standard is published. Presumption of conformity is not. EN 18286 reached publication in July 2026, and publishing a European standard is not the same act as citing it in the Official Journal. What it settles is which records a high-risk provider will be asked for — and a quality management system is the one obligation that cannot be assembled after the fact. The AI Act Omnibus deferred the classification, not the architecture The Digital Omnibus on AI moved the high-risk obligations to December 2027 and August 2028 and left Article 50 running from 2 August 2026. It attaches to how a system is built and surfaced rather than to what it is used for, which is why an ordinary enterprise assistant sits inside the deadline that did not move. Not every AI system needs the same governance Organisations apply one control set to everything they call AI, which is simultaneously too heavy for a summariser and too light for an agent with write access. A written threshold fixes both, and it takes two questions. Someone else's capital cycle is your renewal risk Enterprise AI is being served from infrastructure funded by an investment cycle that analysts expect to run past a trillion dollars a year. Whatever happens to that cycle happens to your unit costs, and almost no AI business case has been tested against it. If a model cannot cite you, you are not in the answer A growing share of the questions your buyers ask are answered by a system that reads a handful of sources and summarises them. What gets read is decided by properties of your published material that are within your control, and mostly are not being controlled. Where your training data came from is now a balance sheet question Courts have begun separating the act of training from the act of acquiring the material, and the money has landed on acquisition. That distinction moves the diligence question from what a model does to where its inputs were obtained. Establish the statistical baseline before you buy a GPU A dull regression, run first, is the cheapest insurance policy in applied machine learning. It either tells you the expensive model is unnecessary, or it gives you the only number that can prove the expensive model was worth buying. The index moved to agents. Your procurement questions have not. The most widely cited composite measure of model capability now weights agentic task completion above everything else. That is a reasonable reflection of where the field went, and it makes a leaderboard position an even weaker answer to a buyer's question. Who is the provider? The question that decides your obligations Most organisations describe themselves as users of AI systems and assume the heavy obligations sit with whoever built the model. Several ordinary engineering decisions move that line, and none of them looks like a legal decision when it is made. Two governance blocs, one supplier list The World Artificial Intelligence Cooperation Organization, founded in Shanghai in July 2026, joins a field already holding the EU regime, the US approach and a set of national rules. For an enterprise the consequence is not geopolitical — deployment location has become a governance attribute.
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