Agentic systems — models that call tools, chain steps, and take actions — break the review patterns most enterprises built for simpler ML. A single-prediction model is easy to audit: one input, one output. An agent making a sequence of decisions is not, and treating it like the old pattern either blocks it entirely or lets it through with no real oversight.
The pattern I use instead has three layers: a tight evaluation harness for the agent's individual tool calls, full logging of the reasoning and action trace (not just the final answer), and human checkpoints placed at the specific steps where a wrong action is expensive to undo — not at every step.
That last part is the one people get wrong most often. Reviewing everything is the same as reviewing nothing; it just moves the bottleneck to an overworked human who rubber-stamps whatever comes through. Reviewing the right two or three points in the chain is what actually keeps velocity and safety in balance.
Getting this right is what makes "yes, and safely" possible instead of forcing a choice between shipping agentic AI and governing it.