Section 087 · Chapter 11, The Confidence Engineer
The Confidence Engineer
The confidence engineer designs evidence systems for AI products: measuring behavior, using AI to test AI, and explaining whether the product is safe enough, useful enough, and reliable enough to ship.
What to do
- Keep humans in the loop for calibration, disagreement, risk, and release decisions.
- Define runnable checks that exercise confidence engineer.
- Set acceptable outcomes and blocker failures for confidence engineer before running the evaluation.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for confidence engineer needed to reproduce work on The Confidence Engineer.
- Report results for confidence engineer by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
The confidence engineer becomes the architect of validation. They design the measurement layer that lets AI-generated products ship quickly without pretending uncertainty disappeared. The title is new-ish. The need is not. Every serious AI product needs someone accountable for the evidence that says whether the system is getting better, getting safer, and getting more trustworthy in the real world.
Continue the conversation
Apply this to your context.
Save your product context once, then open a focused conversation that combines it with this concept.
Cite this page
Jason Arbon. "The Confidence Engineer." Testing AI Knowledge Edition, section 87.
https://jarbon.ai/testing-ai/knowledge/ch087-confidence-engineer.html