The Time is Here for Just-in-Time Systems: Challenges and Opportunities
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 64 references that are in the map. First posted on arXiv 22 May 2026.
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References · 15
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
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- 2022Defining and Characterizing Reward Hackingcited by 184
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- 2024HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Modelcited by 101
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- 2025Context as a Tool: Context Management for Long-Horizon SWE-Agentscited by 38
- 2024Specifications: The missing link to making the development of LLM systems an engineering disciplinecited by 17
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