The argument, in long form.
Where we work out the architecture before we ship it. Board-readable on the way in, engineer-honest on the way down — and reproducible at the bottom, via Gaussia.
Running AI agents on Red Hat OpenShift AI: lessons from a sovereign deployment.
Running AI agents in production is a runtime engineering problem before it is an agent engineering problem — and the runtime layer determines what governance is feasible above.
From a notebook to a fleet: why AI agents need a platform layer.
The first AI agent is not the hard one. The fourth is. When one agent becomes a fleet, the platform underneath them is what makes it operable.
Why governing AI agents end-to-end is now a board-level concern.
When AI agents make decisions a person used to be accountable for, governance reaches the boardroom. The six properties every audit committee should ask about.
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