Why do your coding agents keep getting lost in large repositories?

2026-06-11 · Source: AIModels.fyi - Aimodels.substack.com · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

The SWE-Explore benchmark addresses a critical measurement problem in evaluating coding agents, specifically how existing benchmarks like SWE-bench obscure the distinct capabilities contributing to bug-fixing success. While agents show improved overall success rates, it is unclear whether gains stem from better repository exploration, accurate line localization, or superior patch synthesis. SWE-Explore isolates the "repository exploration" phase, defining it as an agent's ability to efficiently return a ranked list of relevant code regions within a fixed line budget. This granular approach allows for diagnosing specific bottlenecks, differing from traditional file-level code search by focusing on line granularity and bug-specific relevance. Ground truth for this benchmark is derived by analyzing the "trails" of files and line ranges examined by agents that successfully resolve issues.

Key takeaway

For AI Engineers developing or evaluating coding agents, understanding the specific failure modes is crucial. If your agents struggle with bug resolution, you should consider isolating and benchmarking their repository exploration capabilities using methods like SWE-Explore. This allows you to diagnose whether failures stem from poor code discovery rather than patch generation, guiding targeted improvements to agent architecture or training data.

Key insights

Current coding agent benchmarks conflate distinct skills; SWE-Explore isolates repository exploration for granular evaluation.

Principles

• Decompose complex problems into measurable parts.
• Holistic metrics can mask underlying bottlenecks.
• Ground truth can be inferred from successful task completion.

Method

SWE-Explore defines repository exploration as a retrieval problem: agents return a ranked list of relevant code lines within a fixed budget. Ground truth is derived from successful agent execution paths.

In practice

• Evaluate agent exploration before patch synthesis.
• Prioritize line-level relevance over file-level search.
• Analyze agent "trails" for diagnostic insights.

Topics

• Coding Agents
• Repository Exploration
• SWE-Explore Benchmark
• Bug Fixing
• AI Evaluation
• Code Search

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• SWE-Explore: Benchmarking How Coding Agents Explore Repositories — SWE-Explore benchmarks coding agents' line-level repository exploration, isolating it from end-to-end repair outcomes.
• Benchmarking AI Agents on Kubernetes — AI coding agents struggle with system-wide impact analysis, often providing incomplete fixes despite effective code retrieval.
• Beyond SWE-Bench Pro - Where do Agents go from Here? — New benchmarks are crucial for advancing AI coding agents beyond simple issue resolution to complex system understanding and improvement.
• Maintainability sensors for coding agents — Maintainability sensors, combining computational and inferential tools, significantly enhance AI coding agent output quality and human review efficiency.
• When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents — LLM agents struggle with dynamic replanning and error recovery when tools fail, a problem unaddressed by model scaling.
• 10 GitHub Repositories To Master Claude Code — Mastering Claude Code requires understanding its ecosystem beyond basic prompting, leveraging structured agents and workflows.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AIModels.fyi - Aimodels.substack.com.