Defining Failure Boundaries and When to Abstain
Explore how to establish failure boundaries in AI-driven features by assessing the stakes and reversibility of errors. Learn when a model should act autonomously, flag outputs for human review, or abstain altogether to maintain feature reliability across varying contexts.
In the previous lesson, one row of the table landed on “use AI, paired with human review” for deciding whether a conversation needs escalation to a manager. This lesson builds the actual reasoning behind that pairing, how we decide when a model should proceed on its own, when it should say it isn’t sure, and when a person needs to be in the loop before anything happens. By the end, we’ll have a framework that produces a different, correct boundary for different parts of the same feature.
In this lesson, we will cover:
Why a model’s self-reported confidence isn’t something we can build a boundary on
Two questions that actually decide the boundary, severity, and reversibility
A framework combining both, worked through for all four combinations
How the same framework shows up in real products
One feature that needs four different boundaries, depending on what it’s doing
The three ways engineers get this wrong
Why “confidence” is the wrong lens
Ask a model how confident it is in an answer, and it will give us a number. That number comes from the same process that generated the answer, predicting plausible, fluent text. It doesn’t come from anything that checked the answer against reality. A model has no reliable internal signal for “I actually know this” versus “this sounds right,” which is exactly why it can state a wrong fact, a wrong citation, or a wrong policy detail in the same confident, ...