Unverified50% confidenceOpinionExact time
Coding agents are one of the scenarios with the greatest prefill pressure, because code tasks have extremely long contexts (the entire repository, multiple files, error logs, conversation history), often tens of thousands of tokens.
1
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50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
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Related Claims
UnverifiedOne of the core challenges of the harness is context window management, as real-world codebases can easily run to millions of tokens, far exceeding the context length limits of current models.76% similarUnverifiedCoding agents stuff entire code files, function dependencies, and conversation history into the context window to send to the model, causing a single refactoring operation to potentially trigger dozens of API calls, with cumulative token consumption reaching several to tens of times that of manual coding.73% similarUnverifiedWhen an agent works on code, the system prompt, tools, skills, conversation history, and project code compete for the same context window, with only approximately 5,000 to 10,000 lines of code fitting when actually editing files.73% similarUnverified编码智能体的单次任务可能涉及数十次甚至上百次的 LLM 调用和系统操作71% similarUnverifiedThis type of orchestration approach is more suitable for scenarios with long task cycles and heavy context dependence, such as continuously maintaining a code repository, tracking repeatedly adjusted configurations, and handling processes across multiple external systems; for lightweight one-question-one-answer needs, introducing an orchestration layer instead adds complexity.70% similar
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