Articles
Agents need identities, not API keys The fastest way to get an agent working is to give it a service account with broad access. That decision is made in an afternoon, is almost never revisited, and turns every later security question into one that has no good answer. Retrieval is an access-control problem wearing a search interface The most common serious failure in enterprise retrieval systems is not a wrong answer. It is a correct answer, assembled from a document the person asking was never entitled to read, and no quality metric will ever detect it. Every tool description is executable text Connecting an agent to a tool server hands a third party a piece of writing that your model will read as instructions. That is not a configuration file. It is code with a supply chain, and almost nobody is reviewing it as one. Re-embedding is a migration, and nobody plans it Changing the embedding model invalidates every vector in the index, and the index is usually the only copy of how documents were chunked. It is treated as a configuration change, and it is closer to a database migration with no rollback. Energy has become an architectural constraint, not a sustainability line The limit on AI infrastructure has moved from capital to power delivery, and that changes where inference can be placed, what it costs and how quickly capacity can be added. It belongs in the design review, not the annual report. Proof, attribution, and the work somebody else can check Mathematicians have published a declaration on what AI must not be allowed to erode in their discipline. The three values they name translate almost directly into what an enterprise should require of any deliverable produced with a model. A smaller model on your own hardware is a governance decision Self-hosting an open-weight model is usually argued as a cost saving and bought as a sovereignty control. Both framings hide the thing that actually changes, which is who becomes responsible for behaviour that used to be somebody else's problem. Write down what would make you stop Almost every AI programme can describe what success looks like. Very few can state the result that would end the work, and a project that cannot be stopped by evidence is not being evaluated — it is being funded. Most pilots return nothing, and the model is not the reason The widely quoted finding that almost no enterprise AI pilot produces a measurable financial result is not a verdict on model capability. It is a description of what happens when a tool is bought without changing the workflow it was bought to change. Your model retires before your system does Enterprise systems are built to last a decade and the models inside them are supported for months. Nobody owns that mismatch, and it surfaces as an unplanned migration on a date chosen by a supplier. ISO/IEC 42001 is a management system, not a badge Organisations buy the standard expecting a control checklist and receive an operating model instead. The distinction decides whether the certificate is worth anything eighteen months later, when the AI estate has changed and the documents have not. The evaluation set is the asset. Build it before the system. Teams treat evaluation data as something assembled to check a build. Reverse the order — the set is the durable artefact, and the system is the disposable one, because the model underneath it will be replaced within two years.
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