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LLMs are used only for focused judgment tasks, and outputs are validated against predefined schemas before downstream execution.\n  We evaluate this approach on two software engineering workloads using three configurations: monolithic execution, static decomposition with fixed subtasks and no runtime branching, and runtime-struct","title":"Runtime-Structured Task Decomposition for Agentic Coding Systems","url":"https://arxiv.org/abs/2605.15425","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.15425v1 Announce Type: cross \nAbstract: Agentic coding systems increasingly use large language models (LLMs) for software engineering tasks such as debugging, root cause analysis, and code review. However, many existing systems encode task logic, execution flow, and output generation inside monolithic prompts. 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