Beyond fixed rules
A customer emails a photo, describes an intermittent fault and mentions that access is restricted after lunch. The message contains useful information, but it arrives in no particular order. Your existing automation needs somebody to interpret it before it can do its job.
An AI-assisted workflow could extract the relevant details, prepare a summary and flag questions for the scheduler. The scheduler approves the record; familiar automation takes over. That is a meaningful improvement even if nobody calls it an agent.
Now change the task. Suppose the system must investigate missing details, consult availability and choose a next action within defined limits. An agent could help coordinate that more flexible sequence. Its usefulness depends on whether it can make those choices reliably enough for the consequences involved.
The distinction shows up in who controls the next step. A fixed workflow follows an agreed route. An agent can select actions as it proceeds. That flexibility creates possibilities, along with more behavior to test and supervise.
The difficult cases
Return to the supplier’s presentation. A successful happy-path example tells you that something is possible. Ask what happens when the customer gives two addresses, the scheduling tool is unavailable or the request includes a service you do not provide.
Does the system ask a sensible question? Does it hand the issue to someone? Can it stop before making a commitment it cannot keep? Who can see why a job was assigned or a message was sent?
These questions are particularly important when a system can change records, communicate with customers or trigger paid work. A convincing answer includes what the system will not decide alone and how an employee can correct it.
You may discover that the proposed agent is mainly a sequence of familiar automations with an AI step in the middle. That does not make it worthless. It gives you a more accurate basis for judging its value and price.
A fair comparison
Give competing approaches the same set of representative requests. Include incomplete messages, exceptions and cases where the right answer is to involve a person. Compare completed work, correction effort, response time and the cost of keeping it running.
A rules-based approach may be strongest when inputs and decisions are stable. AI interpretation may help when information arrives as language, images or inconsistent documents. An agent may earn its place when choosing a route is a recurring part of the task.
You can combine those approaches. A maintenance company might use AI to interpret the email, an employee to confirm the service requirement and fixed rules to create the approved job. It could introduce more autonomy later if evidence supports it.
Your next decision
The supplier’s terminology matters less once you can describe the work you expect the system to complete. You can ask for a bounded trial, agree who reviews exceptions and make a decision based on results your team understands.
AI agents can offer something beyond repackaged automation. Whether your business needs that additional flexibility is a separate question, and one worth answering before it becomes a larger operating commitment.
Bring Superfactor a workflow you have been told needs an agent. We can help distinguish the useful capability from the label and design the simplest approach that can do the job.
