When enterprises put AI into a workflow, "human in the loop" often gets designed as a button: approve, reject, edit. That framing treats oversight as UX. In production, it is an authority model.
Which prices can an agent set alone? Which cases need a manager? Which postings need finance? Those questions are not wireframe choices. They are policies about risk, audit, and who is accountable when the work is wrong.
Authority models also change over time. Early deployments keep tight thresholds. As evals show stable quality, autonomy expands, and when models or business rules shift, thresholds may tighten again. Continuous evaluation is how that dial moves without guesswork.
This is why deploying work output differs from deploying software. When you ship a tool, the user owns the outcome. When you ship agent work into a process, the deployment layer shares ownership of quality after day one, including escalation paths, evidence of decisions, and recovery when a run fails mid-step.
At IPDigital we treat HITL as part of governance and operate, not a chatbot feature. The same discipline that keeps NOC/SOC and platform delivery accountable applies when agents share the process with your teams.
If you are mapping a first production workflow, start with: what the agent may do alone, what must escalate, how you will measure quality, and who owns the exceptions after go-live.
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