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How to trust an AI-driven QA process | Product Builders | AI-Native #5

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How to build an AI QA process that saves the most – and uses human time only where it counts?

AI doesn't wait for a bug report. In AI-native QA teams, the agent doesn't just run tests – it writes them, maintains them, reacts to code changes, and decides what still needs checking. The catch: code is still the final judge of quality – except now the code is asking itself whether it did a good job.

In this Product Builders | AI-Native session, Piotr Majchrzak (co-CEO, Boldare) and Milena Cylinska (QA Lead & Test Automation Engineer) show how to build a QA process where a human still knows what to trust and what not to – even when AI handles most of the testing.

You'll learn:

how to spot that "all tests passed" can be a false sense of security rather than proof of quality, exactly where to place checkpoints in QA automation so AI never grades its own work unsupervised, how to design a system that earns more autonomy over time – starting small and expanding only based on data, not declarations.

Coming from a team running this in production, the session skips the theory and gets straight to what actually works: a testing process you can trust, and know exactly why

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