Dissecting model behavior through agent trajectories

Abstract

AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through agent harnesses. Therefore, a gap between model assumptions and harness behavior can easily prevent the model's full capabilities from translating into agent performance. We formalize this as the `intent-execution' gap: the mismatch between what the model intends and what the harness executes, and vice versa. We argue that minimizing this intent-execution gap is as important as other aspects of harness design such as tools and execution loops. To illustrate the impact of this harness-model alignment, we develop a simple and customizable harness called `Simple Strands Agent' (SSA). SSA aims to find the bulk of common patterns which generalize across different model families (such as Claude, Gemini, GPT, Grok, Qwen), as well as a small number of model-specific preferences. We make two contributions: (i) we \textbf{reproduce or improve on the pass@1} performance reported by diverse model-provider families on popular agentic benchmarks (SWE-Pro, SWE-Verified, Terminal-Bench-2, and Deep-SWE), and (ii) building on an \textbf{analysis of 154\texttt{k} trajectories generated by SSA}, we look beyond the \texttt{pass@1} numbers which tend to be relatively even across frontier models. By representing agent trajectories in code state-spaces, we compute solution distance and backtracking to observe model-level differences in problem-solving behavior. Finer-grained metrics such as edit frequency, testing activity, and phase-transitions reveal how individual models allocate effort across different stages of problem solving.