# AGENTSAFE: A Unified Framework for Ethical Assurance and Governance in Agentic AI

arXiv: https://arxiv.org/abs/2512.03180 · HTML: https://mlsanity.com/paper/2512.03180/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 2 of 2)

- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)

## Built on by (most cited first, 10 of 10)

- [Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents](https://mlsanity.com/paper/2602.22302/) (2026)
- [A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework](https://mlsanity.com/paper/2604.23338/) (2026)
- [Faramesh: A Protocol-Agnostic Execution Control Plane for Autonomous Agent Systems](https://mlsanity.com/paper/2601.17744/) (2026)
- [Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems](https://mlsanity.com/paper/2605.27628/) (2026)
- [When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting](https://mlsanity.com/paper/2606.16465/) (2026)
- [From Craft to Kernel: A Governance-First Execution Architecture and Semantic ISA for Agentic Computers](https://mlsanity.com/paper/2604.18652/) (2026)
- [Behind EvoMap: Characterizing a Self-Evolving Agent-to-Agent Collaboration Network](https://mlsanity.com/paper/2605.25815/) (2026)
- [Beyond Component Testing: Validating Agentic AI Systems](https://mlsanity.com/paper/2607.29405/) (2026)
- [A Policy Algebra for Trust-Preserving Agentic AI Execution](https://mlsanity.com/paper/2608.16402/) (2026)
- [AgentStripes: An Evidence-Graded Maturity Model for AI Agents](https://mlsanity.com/paper/2610.18603/) (2026)

## Abstract (arXiv)

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.