Framework for Governed Orchestration and Adaptive Lifecycle Management of Hierarchical Artificial Intelligence Agents

Abstract

This document describes a framework for governing and managing hierarchical artificial intelligence (AI) agent systems. The framework addresses various limitations of current approaches, such as static architectures, naive delegation, high operational costs, lack of auditability, security vulnerabilities, and poor conflict handling. The framework includes a primary orchestrator node (PON) for task decomposition, a delegation and provisioning engine (DAPE) for agent selection and provisioning, specialized execution agents (SEAs) for task execution in secure sandboxes, a shared context manifold (SCM) for collaboration, a synthesis and reconciliation module (SRM) for output assembly, a causal traceability ledger for auditability, and a performance-driven optimization loop for continuous improvement. The framework enables dynamic adaptation, intelligent agent selection, built-in governance, stateful collaboration, full auditability, and self-evolution