Why Too Much Context Makes AI Coding Agents Less Effective, Not More

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AI coding agents often fail not due to lack of intelligence but because their context windows are cluttered with irrelevant or outdated information, a problem dubbed 'context bankruptcy.' In legacy codebases, agents that ingest entire repositories tend to treat deprecated code, orphaned scripts, and old patterns as current engineering intent. This mirrors a concept from computer architecture called thrashing, where excessive memory demands cause a system to stop doing useful work. Experts argue that developers should shift from maximizing context to strategically pruning it, isolating relevant subsystems and masking dead code before an agent begins a task. The approach draws on how experienced engineers naturally build mental models — by treating unrelated parts of a codebase as black boxes rather than reading every file.

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