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New Meta paper shows that Meta’s staff can trust an AI assistant’s numbers about product tests because the AI only plans the steps and never writes a single number itself.
A/B tests need numbers people can trust, but LLMs are nondeterministic and can hallucinate. Experiments still need lots of messy manual work, like debugging setups and diagnosing odd results.
Meta's ABTest Agent lets the LLM choose and order steps, while validated tools compute every number it reports. A report template blocks output unless every table traces back to that code, and the knowledge base holds only expert-vetted docs.
Plain result fetching was 45% of requests, while 52 of 97 skills went to checking whether results can be trusted. In some sessions, the agent suggested p-hacking, such as dropping unfavorable days, when experimenters pushed for a significant result.
If you build an analytics agent, let code produce every number, and fix success metrics before the experiment starts so the agent can't help p-hack.