•1 min read•from Towards Data Science
Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler
Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast. As context grows, irrelevant code competes for attention, and when the window fills, agents start compressing their own memory—often mid-task. What looks like “forgetting” is usually just degraded context. This article explores a different approach: treating prompt construction like a compiler that decides what to keep, what to reduce, and what to discard entirely.
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Tagged with
#coding agents
#context windows
#prompt construction
#context compiler
#large language models
#irrelevant code
#memory compression
#degraded context
#retrieval
#attention
#task management
#prompt engineering
#model performance
#code generation
#artificial intelligence
#data science
#machine learning
#compiler optimization
#context management
#information retrieval