Inteq's Agentic AI Q&A Series
Question: How do you decide which decisions an AI agent can make and which require human approval?
Answer: You set the autonomy boundary by two factors: the consequence of being wrong and the reversibility of the action. Low-stakes, easily reversible, high-frequency decisions are strong candidates for full agent autonomy. High-stakes or irreversible decisions warrant human approval. That is the principled basis, and it holds up far better than an arbitrary, case-by-case judgment that gets second-guessed.
I layer confidence on top of those two factors. An agent should escalate whenever its certainty is low, even inside a category that is otherwise fully autonomous. Confidence-based escalation is one of the most effective authority boundaries you can put in place.
The discipline is avoiding both failure modes. Over-automation lets agents act beyond their competence and creates risk; under-automation routes trivial decisions to humans and erases the efficiency gain you were trying to capture. The boundary should be explicit, documented, and revisited as evidence and confidence accumulate, set deliberately, not once and forgotten.
Specifying autonomy boundaries and the decisions an agent is authorized to make is exactly what we cover in Inteq’s Analyzing & Specifying AI Agent Business Requirements training course.
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