---
title: "SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering"
description: "SWE-agent asks how much software-agent capability comes from the language model and how much comes from the interface around it. Read it to see why summarized search, 100-line file windows, precise edits, lint feedback, and compressed history help one fixed model repair more repositories—and why a 12.47% resolve rate is mechanism evidence rather than production autonomy."
canonical_url: "https://fanout.sh/daily/2026-09-26-swe-agent-computer-interfaces"
md_url: "https://fanout.sh/daily/2026-09-26-swe-agent-computer-interfaces.md"
last_updated: "2026-09-26"
access: "public"
---

# SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

SWE-agent asks how much software-agent capability comes from the language model and how much comes from the interface around it. Read it to see why summarized search, 100-line file windows, precise edits, lint feedback, and compressed history help one fixed model repair more repositories—and why a 12.47% resolve rate is mechanism evidence rather than production autonomy.

## Paper details

- Authors: John Yang et al.

- Venue: NeurIPS 2024

- Track: AI Research

- Difficulty: Medium

- Reading time: 35 min

- Original paper: https://proceedings.neurips.cc/paper_files/paper/2024/hash/5a7c947568c1b1328ccc5230172e1e7c-Abstract-Conference.html

## What you will learn

- Why an agent-computer interface controls both the actions a model can express and the repository state it can observe.

- How the search, viewer, editor, linting, and history ablations isolate interface choices around a fixed model.

- How to read SWE-bench pass@1, API cost, sandboxing, patch quality, and manually tuned interfaces without turning a benchmark result into a deployment claim.

## Continue exploring

[Compare the ReAct loop in Fanout](https://fanout.sh/daily/2026-08-10-react-reasoning-acting): Connect SWE-agent's software-specific workbench to the more general reasoning-and-acting loop that alternates model actions with environment observations.

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