A Governance Framework For Agentic AI: Mitigating Systemic Risks In LLM-Powered Multi-Agent Architectures

Abstract

The unique integration of Large Language Models (LLMs) as the reasoning center inside Agentic Artificial Intelligence (AAI) systems exposes new, systematic hazards that current research is totally ill-fitted to handle. The lack of a consistent, thorough framework that simultaneously addresses the basic problems of LLM unreliability as they spread and grow in autonomous, goal-directed, multi-agent systems reveals a major gap in the literature. These issues reach important, under-investigated fields like avoiding goal misalignment, lowering the danger of opaque decision-making, and guaranteeing strong long-term safety in complicated systems. Clearly establishing moral and legal responsibility and existing means for minimal human supervision are clearly lacking, therefore creating a hazardous hole as these automated systems approach actual deployment. This article provides an innovative, unified framework for responsible development, thus directly tackling this critical need. Specifically intended for LLM-powered agentic systems operating in challenging, high-stakes contexts, the author has presented the Trust, Risk, and Safety Management (TRiSM) governance framework. The core innovation of the framework is the Goal-Constraint Alignment (GCA) mechanism, which dynamically monitors and constrains LLM behavior inside set ethical and safety envelopes, hence acting as a dynamic barrier against both planned and unexpected goal misalignment. Furthermore, we install a Decentralized Oversight Ledger (DOL) to improve transparency and allow realistic accountability. The DOL offers real-time, tamper-proof, auditable tracking of all multi-agent interactions and decisions, therefore enhancing human oversight and establishing a clear chain of custody for agent behavior, which is vital to determine legal responsibility. Studies show a major improvement in systematic safety and a significant decrease in catastrophic failures compared to present baseline systems by verifying the effectiveness of the framework against a fresh collection of high-stakes, multi-agent coordination scenarios. This study offers the essential technical and governance structure needed for the responsible and safe distribution of next-generation autonomous artificial intelligence.