In the field of AI, agent performance is not merely a modeling issue but fundamentally a systems problem. The advanced capabilities of models are realized through agent designs. Thus, the gap between model assumptions and agent behavior can hinder the full potential of the model. We formalize this as the intent-execution gap: the mismatch between what a model intends and what the agent executes.
We argue that minimizing this intent-execution gap is as crucial as other aspects of agent design, such as tools and execution loops. To illustrate the impact of this agent-model alignment, we developed a simple and customizable framework called the Simple Strands Agent (SSA). SSA aims to identify common patterns that generalize across different model families (like Claude, Gemini, GPT, Grok, Qwen) and a few model-specific preferences.
We contribute two main findings: (i) we $\textbf{reproduce or improve on the pass@1}$ performance reported by various model provider families on popular agentic benchmarks (SWE-Pro, SWE-Verified, and Terminal-Bench-2); (ii) building on an $\textbf{analysis of 138k trajectories generated by SSA}$, we look beyond the $\texttt{pass@1}$ numbers which tend to be relatively uniform across frontier models. By representing agent trajectories in code state-spaces, we observe model-level differences in problem-solving behavior. Finer-grained metrics like edit frequency, testing activity, and phase transitions reveal how individual models allocate effort across different stages of autonomous problem-solving.
Blogger's Review: This article delves into the critical issue of alignment between agents and models, emphasizing the significance of consistency between intent and execution for model performance. The development of SSA provides valuable insights into understanding agent behavior across different models, particularly in optimizing agent execution.