AI Governance

TL;DR for operators Most enterprise AI failures do not come from the model being “too small”. They come from the system around the model being too vague. A model gives an answer. The workflow accepts it. Nobody knows whether the reasoning path was valid, whether the data path was stale, whether the tool should have been called, or whether the whole process should be redesigned after repeated mistakes. Then someone asks why the AI confidently did something expensive. Excellent. We have automated the intern, but forgot to hire the supervisor. ...

TL;DR for operators Enterprise AI will not become useful merely because someone bolts a chatbot onto a database and calls the result an “agent”. That is theatre with API keys. The paper behind this article proposes something more sober: a blueprint architecture for compound AI systems in the enterprise, where LLMs are important but not sovereign.1 The core idea is that enterprise AI should be built as a distributed system, not as a heroic model prompt. Streams carry data and control messages. Registries expose existing APIs, models, and datasets as searchable assets. Task planners convert user intent into executable workflows. Data planners work out which databases, documents, models, or transformations are needed. Coordinators execute plans while tracking cost, latency, and quality budgets. ...

TL;DR for operators Most enterprise AI projects are still built around a polite fantasy: give the model a prompt, attach a vector database, sprinkle in Chain-of-Thought, and somehow the system will behave like an experienced employee. That works until the agent meets a problem where the correct answer depends less on general knowledge and more on organisational precedent. ...

TL;DR for operators AI personas are moving from gimmick to operating layer. Not because chatbots suddenly became “real people” — please, let us keep one adult in the room — but because modern LLM agents can now imitate human social behaviour well enough to become useful proxies in controlled business experiments. The useful chain looks like this: ...