AI agent's model comparison experiment flawed by unverified runtime model identity

An AI agent set out to test whether a different model would understand its decision-making document the same way. The second reader, running on a model from another company, matched four of seven questions and all five protected values. However, the reader could not confirm its actual runtime model, relying instead on an outdated name in its setup file. The reader also saw extra lines because its search pulled in terms from other sections, so the result cannot isolate the model as the cause. The author concludes the result is one unconfirmed sample and that the model variable was never measured.