AI workflows fail in two ways: they're so cautious nobody uses them, or they're trusted with things they shouldn't touch. Our default avoids both with one rule.
The rule
Nothing reaches a customer without a human check. Internal steps can run on their own, but anything that sends, publishes or promises something externally goes through a person first.
Try breaking the rule in the Pipeline Builder: put "Slack / Email" after a model with no human review, and it won't let you run it.
Why start there
Language models are very useful and occasionally, confidently, wrong. Inside your business, a wrong classification is cheap to catch and fix. In front of a customer, a wrong answer costs trust, and sometimes more. So we put human judgement where mistakes are expensive and let automation handle the rest.
Log everything
Every automated step records what it received, what it produced and why. That makes three things possible:
- Debugging: when something goes wrong, you can see exactly where
- Evaluation: you can measure how often each step is right
- Earning autonomy: a step that's consistently right can be trusted with more
Earning autonomy, one step at a time
Human review is where you start, not where you have to stay. Once the logs show a step is reliably right, and the cost of an occasional error is acceptable, it can run without approval, with sampling and monitoring in place instead. The decision is made step by step, with evidence.
What this looks like in practice
A typical pipeline: new requests are pulled from an inbox, classified automatically, drafted by a model, reviewed by a person, and only then sent. The person reviewing spends seconds instead of minutes, and customers get fast, correct answers.