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AI coding agents find the right file but miss the exact lines that matter, study shows

Claude Code finds the file. Misses the fix. New SWE-Explore benchmark exposes the gap that's silently killing AI agent deployments.

Reporting by The Decoder 1 min brief
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The KeyNews take

Why it matters

A new benchmark reveals a critical blind spot in production AI coding agents: they excel at file-level search but fail at line-level precision. This matters for leaders betting on autonomous code repair—the capability gap is wider than marketing claims suggest.

The key facts

4 to know

  1. SWE-Explore is first benchmark to separately test code search from code repair

  2. AI agents (Claude Code, Codex tested) reliably locate correct files but miss critical lines within files

  3. Context window limitations identified as root cause of repair failures

  4. Benchmark finding suggests silent failure mode in production deployments

Go to the source

The Decoderthe-decoder.com

Publisher excerpt: AI coding agents like Claude Code or Codex reliably find the right file but miss most of the critical lines within it. The new SWE-Explore benchmark is the first to test code search separately from the actual repair, and it shows that without enough context, even the best fix will fail.
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