Workflow automation follows an agreed sequence of steps. An AI agent uses a model to choose actions and tools within the limits you set. If the steps and decision rules are clear, start by checking whether ordinary automation can do the job.
The useful question is what the work requires: moving known information, interpreting an unfamiliar document, or deciding how to investigate a request. These are different jobs and do not all need the same approach.
What is workflow automation?
Consider a completed website enquiry. The form has labelled fields for the business name, contact details and the type of project. An automation can put those fields into a record, acknowledge receipt and notify the person handling enquiries.
The sequence is known. So are the checks for missing information. You can usually explain what should happen for each situation without asking a model to make a judgement.
Where can AI help?
AI may help when information arrives in a less predictable form, such as a long email, a supplier document or a set of notes. It can produce a proposed summary or structured draft for someone to check.
An agent goes further by choosing steps as it works, such as searching for a related job or deciding which available tool to use. That flexibility needs a reason. Anthropic distinguishes predefined workflows from model-directed agents and recommends starting with a simple approach.
Read Anthropic’s guide to building effective agents.
An invoice audit does not need AI for every step
Take a supplier invoice with a job reference, invoice number and amount. If those details are already in a clean table, a duplicate check can compare the supplier and invoice number using an explicit rule. A missing approval can also be flagged directly.
AI may be useful earlier, when those details have to be read from different document layouts. The proposed values can then be checked against the source document before the normal audit rules run.
For a first demonstration, use a small sample with known issues: a repeated invoice and an invoice without approval. Show the source rows, the reason for each flag and the action a person can take. A flag should help someone investigate, rather than automatically deciding which record is correct.
Where should a person approve the result?
Agree this with the people responsible for the work. A draft summary can be low risk. Sending a customer a price, changing a payment record or approving a supplier invoice has a different consequence.
A practical first build can prepare the record, explain what needs checking and wait for the right person to approve it. Review the exceptions as carefully as the routine cases.
What should you test?
- Correct examples, including the different document layouts or request types the team expects.
- Missing job references, unclear amounts and incomplete requests.
- The same document arriving twice.
- A tool or connection being unavailable.
- The point where the system should stop and ask someone for help.
Keep the original input and the result together so the team can see why an item was flagged. Measure successful completion and correction time as well as how quickly the first draft appears.
How do you choose a first project?
Choose a repeated task with a clear owner, accessible sample inputs and an outcome you can verify. Write down the current process before adding AI. If it is unclear who should approve an invoice today, the software needs that decision before it can handle the approval reliably.
A useful brief names the task, the inputs, the next action, the exceptions and the person responsible. It gives you a basis for choosing simple rules, AI assistance or an agent.