Runtime-Structured Task Decomposition for Agentic Coding Systems
The paper discusses a new architectural approach called runtime-structured task decomposition for agentic coding systems. This method aims to improve efficiency and reduce retry costs in software engineering tasks by managing task partitioning and execution flow through executable control logic. The results indicate significant reductions in retry costs compared to traditional monolithic and static decomposition methods.
- ▪Agentic coding systems utilize large language models for various software engineering tasks.
- ▪The runtime-structured task decomposition approach achieved up to 51.7% lower retry costs than monolithic systems.
- ▪In the Kubernetes root cause analysis workload, the runtime-structured approach reduced retry costs to 436 tokens.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15425 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Software Engineering arXiv:2605.15425 (cs) [Submitted on 14 May 2026] Title:Runtime-Structured Task Decomposition for Agentic Coding Systems Authors:Shubhi Asthana, Bing Zhang, Chad DeLuca, Hima Patel, Ruchi Mahindru View a PDF of the paper titled Runtime-Structured Task Decomposition for Agentic Coding Systems, by Shubhi Asthana and 4 other authors View PDF HTML (experimental) Abstract: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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