The Frontend Reward Loop for Agentic Software

I have been thinking about this after reading the rLLM work on post-training language agents. The big idea in that work is right: if agents are going to improve, they need a loop. Not just inference. A loop of action, feedback, and improvement. What I want to argue in this post is simple: The frontend is the best place to collect the heuristics for that loop. Most teams should push prompt augmentation much further before training an offline reward model. tl;dr Agent quality is now a feedback-loop problem, not only a model-size problem. The frontend is the only place where intent, correction, and outcome are visible together. Product heuristics can drive real gains without starting with offline reward-model training. Prompt augmentation gets most teams very far on short and medium-horizon workflows. Move to offline RL only when prompt gains flatten or long-horizon credit assignment becomes the bottleneck. From static intelligence to live behavior Reasoning models can do very well on sta