I’ve been thinking about what makes an AI workflow truly reliable—not just something that works perfectly during testing. Once real people start using it, you get messy inputs, unexpected edge cases, tool failures, and outputs that look correct but are actually wrong. Adding validations, retries, logging, and human review helps, but it can also make the workflow much more complicated. For those building AI workflows in production, how do you decide when a workflow is reliable enough to release? Do you use a specific success rate, testing process, confidence threshold, or monitoring setup? I’d love to hear what has worked for you and what you learned the hard way.