Define what the agent is allowed to do
Every AI agent needs a scope. Can it draft, classify, recommend, route, approve, or execute? These verbs matter because they define risk. A procurement agent that drafts an RFx question is very different from one that sends supplier communication without review.
Control data access
Data access should follow the same logic as employee access: least privilege, approved systems, clear retention rules, and logging. If the agent does not need a document, system, or field, it should not have access to it.
Use human approval where the business risk is real
The safest enterprise pattern is not full automation. It is controlled automation. AI prepares work and handles low-risk steps, while humans approve exceptions, external communication, commercial decisions, and anything with regulatory or contractual impact.
Log decisions and outputs
Teams need to understand what the agent did, what information it used, and where a human intervened. Logging is not just a technical feature. It is what makes the workflow auditable and easier to improve.
Design fallbacks before launch
Every AI workflow should know what happens when confidence is low, data is missing, a system is unavailable, or the output looks unusual. A good fallback is not a failure; it is part of a reliable operating model.
Measure adoption, not only accuracy
An accurate agent that people do not trust will not create value. Track usage, review rates, overrides, cycle time, and qualitative feedback from the teams doing the work.