Hierarchical Agentic Orchestration for Microservices: A Neuro-Symbolic Framework for Dynamic Workflow Composition in Decentralized Financial Systems

Abstract

The increasing complexity of decentralized financial systems has necessitated advanced orchestration mechanisms capable of managing dynamic, distributed microservices. Conventional orchestration methods tend to be less flexible, scalable and transparent to support real time financial functions. The proposed paper presents a new Hierarchical Agentic Orchestration architecture, which uses neuro-symbolic intelligence to facilitate the dynamism in composing a workflow in decentralized finance (DeFi) systems. The suggested model uses a multi-level hierarchy of intelligent agents such global, domain and local agents which are coordinated by the use of meta-controller mechanisms. The complex workflows can be broken into parts in this structure and also it promotes real time flexibility. The neuro-symbolic layer unites machine learning predictive analytics models with symbolic rule enforcement and compliance with a need to ensure flexibility and interpretability. Moreover, the blockchain and smart contract offer a level of trust, immutability, and auditability, which solve the essential issues of decentralized systems. The implementation is carried out on the microservices-based architecture with an event-driven communication channel, which allows workflow execution to be scaled and resilient. The experimental findings show that there are significant latency, throughput, and accuracy improvements with experimental techniques as opposed to the conventional techniques of orchestration. The system is also highly adaptable to the dynamic load and keeps within the predefined policies. On the whole, the study introduces an all-encompassing and intelligent orchestration paradigm that promotes efficiency, transparency, and reliability of decentralized financial ecosystems, which is the foundation of next-generation autonomous financial systems.