
Introducing Confidence Agent
Confidence Agent is an AI collaborator built into Confidence that works with your flags, experiments, metrics, and project documents.
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Confidence Agent is an AI collaborator built into Confidence that works with your flags, experiments, metrics, and project documents.
Read article
As AI increases individual leverage across the product development stack, the boundaries between PM, design, analysis, and engineering are blurring.

Discovery and delivery are no longer sequential phases. AI collapsed the build phase, and now the teams winning are the ones with the tightest loops.

At Spotify, 42% of experiments are rolled back after guardrail metrics detect regressions. The discipline to discard is more valuable than the ability to ship.

Most A/B testing tools either overestimate the sample size needed for always-valid inference or do not adjust for the sequential test in the sample size calculation at all. We derived a closed-form correction that requires no simulation.

A/B testing maturity has a dangerous middle phase: teams test, dislike the answer, and explain it away. How to spot it and learn to trust your data.

The primary output of a mature experimentation program is better judgment. At Spotify, the learning rate is 64%. The win rate is 12%.

AI-accelerated code production increases the need for experimentation. The validation bottleneck grows with build speed. The fastest learners will win.

A good sample size calculator must match your analysis: sequential testing, multiple metrics, and variance reduction all change the number it returns.

AI made building cheap. It also made bad decisions cheaper to ship. The distance between execution speed and validation speed is the judgment gap.









