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

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.