Automate the boring bit first.
Pick a repeated, bounded process before reaching for an autonomous agent.
Look for repeated handoffs
A useful automation candidate often starts with someone copying the same information between systems. The steps are repeated, the desired result is clear, and the exceptions are already familiar to the people doing the work.
Write down the inputs, the decision rules and the destination. If no one can explain the exceptions, the first task is understanding the process.
Separate rules from judgment
Predictable rules may only need a small integration. An AI model can help with ambiguous text or document classification, but it adds uncertainty that has to be managed. Use the simplest mechanism that solves the problem.
For example, copying approved invoice totals between two APIs does not inherently require a language model. Interpreting a free-form request might, with a review step when confidence is insufficient.
Design the failure path
What happens if an API is unavailable? Can a retry create a duplicate? Who notices when a record cannot be processed? These questions belong in the first version, along with access controls and an audit trail where appropriate.
Measure the actual difference
Record the time spent on the manual process and the frequency of exceptions before automating. After release, compare that with maintenance and review effort. An automation is useful when it reduces the total burden.
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