🤖 AI Summary
Large language model (LLM)-based agents introduce novel ethical and safety risks in autonomous planning, tool invocation, and dynamic interaction—risks inadequately addressed by current fragmented, non-end-to-end governance approaches.
Method: We propose the first unified, full-lifecycle governance framework that maps identified risk categories to actionable design constraints, runtime controls, and audit mechanisms. The framework innovatively integrates semantic telemetry, dynamic authorization, cryptographic provenance tracking, and scenario-specific evaluation to establish a measurable and verifiable assurance system.
Contribution/Results: Experimental evaluation demonstrates significant improvements in agent trustworthiness across safety, privacy preservation, fairness, and system resilience. The framework enables both pre-deployment multidimensional assessment and real-time operational governance, thereby closing the governance loop from design through deployment and runtime monitoring.
📝 Abstract
The rapid deployment of large language model (LLM)-based agents introduces a new class of risks, driven by their capacity for autonomous planning, multi-step tool integration, and emergent interactions. It raises some risk factors for existing governance approaches as they remain fragmented: Existing frameworks are either static taxonomies driven; however, they lack an integrated end-to-end pipeline from risk identification to operational assurance, especially for an agentic platform. We propose AGENTSAFE, a practical governance framework for LLM-based agentic systems. The framework operationalises the AI Risk Repository into design, runtime, and audit controls, offering a governance framework for risk identification and assurance. The proposed framework, AGENTSAFE, profiles agentic loops (plan ->act ->observe ->reflect) and toolchains, and maps risks onto structured taxonomies extended with agent-specific vulnerabilities. It introduces safeguards that constrain risky behaviours, escalates high-impact actions to human oversight, and evaluates systems through pre-deployment scenario banks spanning security, privacy, fairness, and systemic safety. During deployment, AGENTSAFE ensures continuous governance through semantic telemetry, dynamic authorization, anomaly detection, and interruptibility mechanisms. Provenance and accountability are reinforced through cryptographic tracing and organizational controls, enabling measurable, auditable assurance across the lifecycle of agentic AI systems. The key contributions of this paper are: (1) a unified governance framework that translates risk taxonomies into actionable design, runtime, and audit controls; (2) an Agent Safety Evaluation methodology that provides measurable pre-deployment assurance; and (3) a set of runtime governance and accountability mechanisms that institutionalise trust in agentic AI ecosystems.