ACI-Core: How Software Semantics Shape Language Agents' Task Execution

As large language models are increasingly used to operate application software, prior work has largely focused on tool invocation, agent-oriented interface design, and graphical user interfaces, while a deeper question remains underexplored: how do different types of software runtime information affect an agent's task execution? We introduce ACI-Core, which models a stateful application as a system of observable state transitions. Through an external and pluggable layer, ACI-Core turns software information, such as action semantics, execution conditions, and execution history, into controllable experimental variables without modifying the application's business logic. Across multiple controlled experiments, we find that multiple types of information, including execution history and structured goals, affect task completion and execution cost. In MailWorld, exposing a History bundle increased terminal Oracle success by 57.5 percentage points, while exposing a structured Goal increased success by 32.5 percentage points in a separate cohort. Full-event History further reached 95% terminal Oracle success, but used 3.33 times the input tokens of bounded History. The largest observed change in task completion approached 60 percentage points. In a delayed-evidence reuse experiment on τ-bench retail tasks, targeted history retrieval substantially improved first exact-binding reliability, matching full history on first exact binding while using 76.8% fewer input tokens. Our study of ACI-Core suggests that effective software operation by AI agents is shaped by what task-relevant information is available, when it becomes available, and how it is selected and preserved. All experimental code and data are available in the anonymous repository: [https://anonymous.4open.science/r/Your-anonymizations9527-8B71](https://anonymous.4open.science/r/Your-anonymizations9527-8B71).