You change the prompt. Then the output changes. You update a tool. Something else breaks. You modify the workflow. Three weeks later, nobody remembers why. This is why production AI systems need a simple change log. Track: DATE When was the change made? WHAT CHANGED? Prompt, tool, model, workflow, knowledge, permissions? WHY? What problem were you trying to solve? EXPECTED RESULT What should improve? ACTUAL RESULT What happened afterward? ROLLBACK? If things get worse, can you safely revert? You don't need a complicated engineering platform. Even a simple document or spreadsheet is better than: “I think I changed that last Tuesday…”

End of the month is a good time to look at the AI workflows you've built. Pick one. Then put it into one of four categories: KEEP It's working and delivering measurable value. FIX The idea is good, but something is limiting the result. REPLACE The current implementation isn't working, but another approach might. REMOVE It's creating more work than it's saving. Then ask five questions: Did it save time? Did it improve quality? Did people actually use it? How much maintenance did it require? Would we build it again today? That last question can be uncomfortable. But it's useful. The goal isn't to collect more AI workflows. It's to build better operations.

An AI workflow can have a strong model and still fail because one handoff is unreliable. Maybe the model produces the right answer, but the CRM update fails. Maybe a tool returns an unexpected format. Maybe a human reviewer never receives the task. Map the workflow from start to finish: 1. What triggers it? 2. What input does each step need? 3. What does each step return? 4. What happens when the expected result is missing? 5. Who owns recovery when it fails? Don't evaluate only the AI response. Evaluate the whole path from trigger to completed outcome. Where does your workflow most often break: input, model output, tool/API, handoff, or final action?