AI workflows are easy to build and easy to trust too much. The gap between "worked once" and "works reliably" is closed by three unglamorous habits: test cases, a failure log, and cost tracking.
What you'll have by the end: a small set of tests you can rerun, a record of how it fails, and a handle on what it costs, so you know whether to trust it.
1. Write a handful of test cases
Pick 5–10 real examples, including the weird ones: the empty input, the huge input, the ambiguous request, the edge case that always trips things up. Run your workflow on all of them, not just the happy path. This is where most workflows quietly fail.
2. Keep a failure log
Every time it gets something wrong, write down what went in, what came out, and what should have happened. Patterns appear fast, and each one becomes a rule you can add to fix it.
Date | Input | What it did | What it should do | Fix
-----------------------------------------------------------
[..] | [..] | [..] | [..] | [rule to add]
[..] | [..] | [..] | [..] |
Review weekly. Turn repeat failures into explicit rules in
your instructions, and add the tricky input to your test cases.3. Track what it costs
Every run costs something. Note the rough cost per run and multiply by how often you'll run it. A workflow that's cheap once can be expensive at scale, and knowing that up front stops nasty surprises.
The line between a demo and a system
A demo works once, on the example you chose. A system keeps working on inputs you didn't choose, fails loudly instead of silently, and costs what you expect. These three checks are how you cross that line.
The dangerous failures are the ones you don't notice. Build in a check, or a human review, so mistakes surface before they reach a customer.