AURA: An Agent Autonomy Risk Assessment Framework
Lorenzo Satta Chiris, Ayush Mishra
TL;DR
AURA tackles the governance and risk management challenges of deploying autonomous, agentic AI by introducing a gamma-based risk scoring framework that quantifies risk across context-dimension pairs. The architecture combines a modular processing pipeline, HITL oversight, memory-enabled reasoning, and A2H communication to deliver both interpretable risk profiles and actionable mitigations, with two deployment modes: a synchronous web interface for pre-deployment assessment and an autonomous Python package for live operation. Key contributions include the integration of memory and probabilistic risk reasoning with practical mitigations, and interoperability with MCP and A2A protocols, enabling scalable, transparent enterprise adoption. The approach promises robust risk detection and mitigations while balancing computational resources, positioning AURA as a practical tool for governable large-scale agentic AI in real-world settings.
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.