Bridging intent and execution in agentic systems

AI agent performance is not just a modeling problem; it is fundamentally a systems problem. A modern agent combines an LLM with a harness, software that mediates the LLM’s interaction with tools and manages the cycle of reasoning and feedback: you can think of the harness as the operating system around the model. As models improve, the performance bottleneck shifts from the model’s ability to reason to the harness’s ability to translate model intent into actions and reflect execution outcomes back to the model. In a paper we just published on arXiv, "Dissecting model behavior through agent trajectories", we formalize this bottleneck as the intent-execution gap: the mismatch between what the model intends and what the harness executes, and vice versa. For example, in trying to revise code, a model may intend to edit a single instance of a function, while the harness accidentally modifies multiple instances. We show that minimizing this bidirectional gap — without any task-specific tuning — is sufficient to achieve state-of-the-art performance across diverse agentic benchmarks, including datasets that test real-world repository patching (SWE-Pro, SWE-Verified) and interactive terminal environments (Terminal-Bench2). While the most visible components of the harness — such as the execution graph, which controls…