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Governing AI Agents That Do Real Work
Governing an AI agent means defining what it may decide, which sources it may use, how it substantiates answers, when it escalates to a human, and how usage is logged and monitored, treated as a governance design exercise rather than a technical configuration afterthought. An agent given real decision-making latitude without these boundaries explicitly defined tends to behave unpredictably in exactly the edge cases where predictability matters most.
Why "just configure some guardrails" isn't enough
Technical guardrails constrain what an agent can technically do; governance decisions determine what it should do, who's accountable if it gets something wrong, and how anyone would even know something went wrong. Those are organisational decisions, not settings in a configuration panel.
What "how it substantiates answers" actually requires
A defined standard for what counts as sufficient evidence before an agent acts or reports a conclusion, not just an assumption that the underlying model will be right often enough. This matters most for agents making decisions with real consequences, not ones drafting a first version of a document.
Where escalation rules actually get tested
Not in the common cases, which most agents handle fine, but in the edge cases nobody explicitly designed for. Defining escalation triggers in advance, rather than hoping the agent handles ambiguity sensibly, is what determines whether an edge case becomes a minor issue or a real incident.
75% of Boards Admit Their AI Strategy Is More for Show Than Real Steering
We redesign the operating model first, then place AI where it actually creates value, turning isolated pilots into a workforce plan that blends people, flexible talent, machines, and agents.