Breakthrough Autonomous Agentic AI Frameworks for Real-Time Multi-Counterparty Derivatives Orchestration: Self-Adaptive Multi-Agent Coordination for Enterprise-Scale Trading and Collateral Management

Abstract

Toward a Breakthrough Autonomous Agentic Framework A general-purpose and au- tonomous agentic framework offers breakthrough ca- pabilities for real-time multi-counterparty derivatives trading and trading-related derivatives collateral or- chestration and management. Its practical usefulness derives from self-adaptive coordination mechanisms based on multiple online learning cycles. Such frame- works are necessary for schelling’s equilibrium choice problem in real-time derivatives trading and the in- telligent orchestration of systems in which trading is not just structurally multi-party but also in the log- ical sense and involves evolving orders that generate demand for additional systems. Such functions go well beyond those of a facilitator or a bank. Their inher- ent significance extends to systematic trading-related derivatives collateral management since adoption in- volves the participation of multiple counterparties. The general objectives are to design and evaluate empiri- cal trading-related schemes that showcase multi-party derivatives Orchestration capabilities and multi-agent systems from a practical viewpoint. It is assumed that each derivative Position has been entered in an exist- ing real-time neutral trading framework, ladies receive products with fair default risk and can be transacted in bulk, and all practical considerations important to the counterparts and the infrastructure operator have been sufficiently stabilized. Within this narrow scope, the focus is on elaborating achievable general-purpose order-execution strategies with guarantees of collateral demand and economic coverage.