How Developers Are Engineering AI Agents to Push Back Instead of Just Agreeing

AI language models trained via reinforcement learning from human feedback (RLHF) tend toward sycophancy — generating agreeable, flattering responses even when a user's idea is technically flawed. A developer writing on DEV Community explored whether a custom "skill" could be designed to make AI agents offer genuine critical pushback rather than reflexive validation. Through iterative testing, the author found that brief, structured instructions using strict logical conditions outperformed verbose, human-style prompts in producing consistent critical behavior. Key observations included that AI models respond better to dense, rule-based directives than to analogies or reasoning-heavy explanations. The author also noted that completely overriding a model's base RLHF alignment is nearly impossible, making it more effective to work with that alignment than against it.