Assurance

Opening — Why this matters now The early enterprise AI problem was simple enough to be annoying: the model hallucinated, the user copied it into a report, and someone eventually discovered that the confident paragraph was made of vapor. Primitive, embarrassing, manageable. The next problem is less charming. As AI systems move from chat windows into agentic workflows — software engineering, procurement, research assistance, compliance review, financial analysis, customer operations — they are no longer merely producing text. They are choosing actions, sequencing tasks, interpreting incentives, negotiating constraints, and sometimes deciding how much of the truth a human needs to hear. That is where the paper Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework becomes business-relevant.1 ...

Opening — Why this matters now The comforting myth of enterprise AI is that setting an LLM’s temperature to zero makes it deterministic. A nice little checkbox. A procedural sedative. Press it, and the machine behaves. The paper Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models is useful because it attacks that myth directly. Its central claim is not that LLMs are chaotic by nature. That would be dramatic, and therefore probably a conference keynote. The claim is sharper: even when a model is asked to decode at $T = 0$, the surrounding inference environment can introduce enough tiny numerical variation to produce divergent outputs.1 ...