Although Large Language Model (LLM) agents have demonstrated remarkable performance on complex, real-world tasks, the significant bottleneck of execution inefficiencies and static environmental feedback hinders agents’ effective generalization to open-ended scenarios. To address these limitations, this paper introduces Role-Agent, a novel framework that repurposes one LLM to concurrently function as the agent and the environment, thereby enabling a bootstrapped co-evolution. At the core of Role-Agent are two synergistic components: World-In-Agent (WIA) and Agent-In-World (AIW). In WIA where the LLM acts as the agent, we define the reward for an action as the alignment between the agent’s prediction of the next several states and the actual state. These rewards are subsequently grouped by state and optimized, equipping the agent with predictive reasoning capabilities regarding the environment. In AIW where the LLM provides environmental adjustments, we require it to analyze the typical failure modes from failed trajectories, then searches for tasks that exhibit similar failure modes, thereby altering the data distribution to facilitate learning. Extensive experiments conducted on the various benchmarks demonstrate the substantial performance improvements achieved by Role-Agent, yielding an average performance gain of over 4%.