Plenty of work hands a language model the same shell and editor a human uses and assumes that is enough. This paper argues the opposite: agents are a distinct class of user, and the interface itself — what commands exist, how output is shaped — is the lever that moves performance. Just as IDEs were built for human ergonomics, agents need an agent-computer interface (ACI) designed for theirs.
Key Findings
- The agent-computer interface (ACI) is treated as a first-class design surface: compact, purpose-built commands for navigating repos, viewing and editing files, and running tests, paired with feedback trimmed to what the model can actually use.
- Interface design, not just the underlying model, drives results — SWE-agent reaches 12.5% pass@1 on SWE-bench and 87.7% on HumanEvalFix, far above non-interactive LM baselines.
- Ablations show the shape of feedback and the granularity of edit commands materially change agent behavior, including how often it loops or recovers from mistakes.
Who It's For
Great fit if you build or study LM agents that act on real codebases and want evidence that interface engineering pays off as much as model choice. The SWE-agent framework and SWE-bench leaderboard make the work reproducible. Look elsewhere if you need a turnkey coding assistant rather than a research system, or if your interest is model training rather than the agent-environment boundary; the contribution is the interface and its analysis, not a new model.