Coding Agents Need a Context Compiler, Not Just Bigger Windows
Towards Data Science
EDITOR BRIEF
The excerpt argues that many coding agents fail because they build prompts like simple retrieval systems: they keep adding files and hoping the model can sort it out. As the context gets larger, unrelated code gets in the way, and once the window is full, the agent may compress its own memory during a task. The proposed alternative is a compiler-like process that chooses what to keep, reduce, or drop.
INSIGHTS
For learners, this shows that prompt quality matters as much as prompt size. A practical next step is to practice summarizing codebases into only the most relevant files, functions, and decisions before sending them to an AI tool.
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