AURA: An Agent Autonomy Risk Assessment Framework
Abstract
As autonomous agentic AI systems see increasing adoption across organisations, persistent challenges in alignment, governance, and risk management threaten to impede deployment at scale. We present AURA (Agent aUtonomy Risk Assessment), a unified framework designed to detect, quantify, and mitigate risks arising from agentic AI. Building on recent research and practical deployments, AURA introduces a gamma-based risk scoring methodology that balances risk assessment accuracy with computational efficiency and practical considerations. AURA provides an interactive process to score, evaluate and mitigate the risks of running one or multiple AI Agents, synchronously or asynchronously (autonomously). The framework is engineered for Human-in-the-Loop (HITL) oversight and presents Agent-to-Human (A2H) communication mechanisms, allowing for seamless integration with agentic systems for autonomous self-assessment, rendering it interoperable with established protocols (MCP and A2A) and tools. AURA supports a responsible and transparent adoption of agentic AI and provides robust risk detection and mitigation while balancing computational resources, positioning it as a critical enabler for large-scale, governable agentic AI in enterprise environments.
Cite
@article{arxiv.2510.15739,
title = {AURA: An Agent Autonomy Risk Assessment Framework},
author = {Lorenzo Satta Chiris and Ayush Mishra},
journal= {arXiv preprint arXiv:2510.15739},
year = {2025}
}
Comments
10 pages, 2 figures. Submitted for open-access preprint on arXiv. Based on the AAMAS 2026 paper template