You Won't Believe This Click: Content Rewriting for Agentic Choice

Language models are increasingly used as agents to help humans decide what information is surfaced. This usage incentivizes content creators to optimize content in ways that appeal not only to humans, but also to agents that mediate access to them. In this paper, we study selection shifts induced by rewriting in agentic decision-making. Given a set of competing content snippets, we rewrite only one snippet while leaving the rest unchanged, and then measure how it impacts the agent's choice. We operationalize this setup with AgentBait an advisor-rewriter framework in which the advisor learns to propose rewriting strategies and the rewriter revises the snippet. While a rewriter with a fixed prompt improves target snippet selection from 17.1% to 34.8%, our AgentBait raises its selection to 98.5%. We further show that the advisor trained with AgentBait effectively transfers to setups with different agents, languages, and snippets in other domains (e.g., scientific papers). However, higher target selection can reflect unsupported rewrites rather than better content. Adding a reward for support from the original snippet redirects the advisor toward more supported rewriting strategies, revealing a trade-off between factuality and target selection. Together, our results show that once agents mediate access to information, content can be rewritten to be chosen by the agent, even when selection and usefulness diverge.