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<p>High-frequency options markets present a specialized challenge where the computational cost of risk calculation conflicts with the imperative for ultra-low-latency execution. <b>However</b>,<b> existing solutions often prioritize raw speed at the expense of safety</b>,<b> resulting in opaque ‘black-box’ systems unsuitable for regulated environments.</b> This paper introduces Orchestrated Intelligence (OI), a <b>conceptual reference architecture</b> based on an adaptive multi-agent system (MAS) specifically engineered for <b>verifiable options market making</b>. Unlike traditional approaches, OI explicitly addresses the trade-off between sub-10ms latency and <b>operational integrity</b>. The architecture coordinates specialized agents—pricing engines, volatility predictors, and execution algorithms—via a central adaptive orchestrator <b>embedded with mandatory safety gates</b>. We define Orchestrated Intelligence as the emergent capability achieved through the managed, <b>auditable</b> interaction of functionally distinct agents. While primarily theoretical, this work offers a detailed blueprint and simulations demonstrating how <b>verifiable architecture</b> can mitigate adverse selection costs without sacrificing speed. We explicitly acknowledge the engineering challenges of this approach and conclude with a rigorous <b>validation roadmap</b>, outlining the path from conceptual design to formal verification and empirical testing.</p>

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Orchestrated intelligence: an adaptive multi-agent architecture for high-frequency options risk management

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Mohammadreza Ayatollahi,
  • Seyed Mohammadbagher Jafari

摘要

High-frequency options markets present a specialized challenge where the computational cost of risk calculation conflicts with the imperative for ultra-low-latency execution. However, existing solutions often prioritize raw speed at the expense of safety, resulting in opaque ‘black-box’ systems unsuitable for regulated environments. This paper introduces Orchestrated Intelligence (OI), a conceptual reference architecture based on an adaptive multi-agent system (MAS) specifically engineered for verifiable options market making. Unlike traditional approaches, OI explicitly addresses the trade-off between sub-10ms latency and operational integrity. The architecture coordinates specialized agents—pricing engines, volatility predictors, and execution algorithms—via a central adaptive orchestrator embedded with mandatory safety gates. We define Orchestrated Intelligence as the emergent capability achieved through the managed, auditable interaction of functionally distinct agents. While primarily theoretical, this work offers a detailed blueprint and simulations demonstrating how verifiable architecture can mitigate adverse selection costs without sacrificing speed. We explicitly acknowledge the engineering challenges of this approach and conclude with a rigorous validation roadmap, outlining the path from conceptual design to formal verification and empirical testing.