Engineering field notes
Before an AI workflow acts, decide how it can fail.
A working demonstration is the beginning. A useful business workflow also needs boundaries, evidence and a way to recover.
Start with one task and a baseline.
Choose a repeated task with a result that a person can judge. Record how the current process works, including its time and error patterns. This gives a pilot something concrete to improve against.
Give it only the access the task needs.
A document-drafting assistant and an assistant that sends messages or changes records require different controls. Specify which actions require a person’s approval and give each tool a narrow purpose.
Evaluate ordinary and awkward cases.
Use representative examples, incomplete inputs and cases where the right answer is to stop and ask. Keep a record of the source material, expected behaviour and observed result. A fluent answer is not evidence that the workflow is correct.
Make uncertainty visible.
Show users the sources, unresolved questions and important limitations. Avoid turning a failed retrieval into a confident answer. Decide what a person should do when the system cannot complete the task.
Keep the decision to expand separate.
Review quality, operating cost, user feedback and failure handling before giving a workflow more responsibilities. A bounded pilot should produce evidence for that decision, not pressure to expand.
Explore the Data Lab · Discuss a project
Written by DataXLR8. This is practical guidance, not a claim about measured client results.