Externalizing Agent Reasoning into Synthetic Environments

Large language model agents increasingly use tools to perform tasks in software environments. Success requires applying rules to the current state and anticipating how an action affects later steps. We introduce EARIS, a framework that externalizes this reasoning into executable synthetic environments. First, a common formulation describes an environment through its interface, internal states, and transition function. A synthesis process combines available records, implementations, and rules into an executable environment, then tests and refines it. This environment can retain partial state without replicating the entire system. Second, the agent tries proposed actions on disposable copies and uses feedback about results, state changes, and tested follow-up actions to revise its decisions before real execution. Real interaction updates the stored synthetic state without training the agent. Across six benchmarks, GPT-5.4 task success rises from 22% to 49% on airline, 5% to 56% on TravelPlanner, and 22% to 55% on SOPBench when rules are hidden from the agent but available to the environment. Controls separate the benefits of rule access, checks, and feedback about later actions, while identifying settings where simple checks or explicit reminders suffice.