The Time is Here for Just-in-Time Systems: Challenges and Opportunities
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916040846344192 |
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| author | Liu, Shu Krentsel, Alexander Agarwal, Shubham Cemri, Mert Mao, Ziming Ponnapalli, Soujanya Dimakis, Alexandros G. Ratnasamy, Sylvia Zaharia, Matei Parameswaran, Aditya Stoica, Ion |
| author_facet | Liu, Shu Krentsel, Alexander Agarwal, Shubham Cemri, Mert Mao, Ziming Ponnapalli, Soujanya Dimakis, Alexandros G. Ratnasamy, Sylvia Zaharia, Matei Parameswaran, Aditya Stoica, Ion |
| contents | Core systems like key-value stores have historically taken years to build, and are designed to be general so as to amortize cost across deployments, paying a significant performance cost. We argue that LLM-based coding agents now make a different approach tractable: Just-in-Time Systems, in which the entire system is synthesized from scratch, specialized to the environment, workload, and required system properties. We present a JIT system synthesis pipeline, Jitskit, and explore its effectiveness in synthesizing key-value stores from spec cards that span different YCSB workloads, deployment constraints (e.g., compute resources), and system properties (e.g., consistency and durability). Jitskit iteratively refines a system implementation to match the specification against an evolving evaluation test suite. The resulting synthesized systems are performant, beating comparable state-of-the-art systems on 18 of 18 specs tried, by up to 4.6x over the best off-the-shelf baseline on the most favorable spec. Naively running Claude Code either reward-hacks or underperforms Jitskit by up to 5.4x. We discuss the challenges we overcame in building Jitskit and our key takeaways. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24096 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Time is Here for Just-in-Time Systems: Challenges and Opportunities Liu, Shu Krentsel, Alexander Agarwal, Shubham Cemri, Mert Mao, Ziming Ponnapalli, Soujanya Dimakis, Alexandros G. Ratnasamy, Sylvia Zaharia, Matei Parameswaran, Aditya Stoica, Ion Databases Artificial Intelligence Distributed, Parallel, and Cluster Computing Software Engineering Core systems like key-value stores have historically taken years to build, and are designed to be general so as to amortize cost across deployments, paying a significant performance cost. We argue that LLM-based coding agents now make a different approach tractable: Just-in-Time Systems, in which the entire system is synthesized from scratch, specialized to the environment, workload, and required system properties. We present a JIT system synthesis pipeline, Jitskit, and explore its effectiveness in synthesizing key-value stores from spec cards that span different YCSB workloads, deployment constraints (e.g., compute resources), and system properties (e.g., consistency and durability). Jitskit iteratively refines a system implementation to match the specification against an evolving evaluation test suite. The resulting synthesized systems are performant, beating comparable state-of-the-art systems on 18 of 18 specs tried, by up to 4.6x over the best off-the-shelf baseline on the most favorable spec. Naively running Claude Code either reward-hacks or underperforms Jitskit by up to 5.4x. We discuss the challenges we overcame in building Jitskit and our key takeaways. |
| title | The Time is Here for Just-in-Time Systems: Challenges and Opportunities |
| topic | Databases Artificial Intelligence Distributed, Parallel, and Cluster Computing Software Engineering |
| url | https://arxiv.org/abs/2605.24096 |