Why do AI coding agents make errors when editing files?

Three main breaking points: line drift (insertions/deletions shift subsequent line numbers), lost-in-the-middle (middle lines silently disappear in large files), and match failure (if a formatter runs, the expected text no longer exists).

Why is Script Generation the most efficient file editing strategy?

Script Generation does not include file content in tokens, only sed commands. On a 1053-line file with 10 changes: 7,000 tokens, 10 seconds. Same operation with Sequential Edit takes 25,000 tokens and 65 seconds.

What is the difference between Atomic Write and Sequential Edit?

Sequential Edit makes a separate Edit call per change and carries line drift risk. Atomic Write rewrites the entire file in a single Write call but has the highest token cost.

What is the edit-guard hook and how does it work?

edit-guard is a deterministic protection layer using Claude Code's PostToolUse hook. It runs automatically after every Edit or Write and performs 3 checks: consecutive edit counter, line count verification, and lost-in-the-middle detection.

Which editing strategy should I use based on file size?

For 1-2 changes, Edit works at any file size. For 3-5 changes, use Script Generation or Unified Diff. For 6+ changes, Script Generation is recommended. Atomic Write is only suitable for files under 200 lines.

Why AI Agents Break Files: Practical Strategies and Tests

I benchmarked 5 file editing approaches used by AI coding agents across 20 scenarios. Sequential Edit, Atomic Write, Bottom-up Edit, Script Generation, and Unified Diff, which works when?

TL;DR

AI coding agents can use 5 different strategies when editing files. In a 20-scenario benchmark, Script Generation was the most efficient (6K tokens, 10s), Atomic Write the most expensive (43K tokens). For deterministic protection, the edit-guard hook catches line loss, truncation, and consecutive edit errors.

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