A fixed script or rules engine handles the cases it was told about and breaks on the rest. It either throws an error, silently does the wrong thing, or sits there until someone rewrites the rules. An agentic system works differently. Given a goal and access to the real tools a business already uses, it reads what's actually in front of it, decides on a reasonable next step, acts, and checks whether that step worked. When a case doesn't match anything it recognizes, it can back off and hand the decision to a person instead of forcing an answer. That difference, reasoning about the specific case in front of it instead of matching it against a fixed list, is what makes exception-heavy work automatable at all.
Before building anything, we map the manual work as it actually happens, including the exceptions, and say plainly which of them a system can own and which it can't. Some edge cases repeat often enough that a system can learn to handle them: a missing field, a delayed response. Others are true one-offs that depend on context nobody wrote down: an exception someone made once for a specific reason, a judgment call with legal or financial weight attached. Those stay with a person. The system we build routes them there cleanly instead of guessing, and because we keep operating the system afterward, we can watch which exceptions keep recurring and widen what it handles over time.
This doesn't fit every process. If almost every case is different and there's no real pattern underneath the exceptions, automation won't help much, and it can make things worse by acting confidently on a case it doesn't actually understand. It also doesn't fit when the judgment involved lives entirely in one person's head and can't be described to a system even roughly. In those situations the honest move is to automate the repeatable parts around the edges (data gathering, status updates) and leave the actual decision with the person making it today.