Engineer enforces falsifiability rules to curb AI agents' overconfident outputs

A software engineer running an AI agent fleet has adopted a strict decision-making framework that requires every claim to include an explicit falsification condition before it can inform any consequential action. Inspired by philosopher Karl Popper's falsifiability principle, the system rejects outputs like percentage-based predictions from language models, reserving numerical probabilities for separately validated statistical models. Claims are categorized into four tiers — fact, inference, unidentified correlation, and spurious — each with defined rules on whether they can drive decisions. Every falsifiable claim must specify an observable condition, a measurable threshold, and a concrete evaluation date. The engineer argues this structural constraint, rather than better prompting, is the real fix for AI systems that produce authoritative-sounding but analytically hollow outputs.