How Poor Context Management Degrades AI Coding Agent Performance

AI coding agents often underperform not due to lack of intelligence but because their context windows are cluttered with irrelevant data such as outdated chat logs, legacy code, and boilerplate files. Large language models process all tokens with equal initial weight, forcing them to expend computational effort filtering noise, which lowers signal-to-noise ratio and increases hallucinations. Developers can improve agent accuracy by treating the context window like a physical workspace and pruning it regularly. One recommended practice is asking the AI to summarize key decisions and project state before clearing a session, then feeding that compact summary into the next chat instead of the full transcript. Starting fresh sessions when switching between unrelated tasks is also advised to prevent irrelevant context from diluting the model's focus.