With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
Agentic AI systems present both significant opportunities and novel risks due to their capacity for autonomous action, encompassing tasks such as code execution, internet interaction, and file modification. This poses considerable challenges for effective organizational governance, particularly in comprehensively identifying, assessing, and mitigating diverse and evolving risks. To tackle this, we introduce the Agentic Risk & Capability (ARC) Framework, a technical governance framework designed to help organizations identify, assess, and mitigate risks arising from agentic AI systems. The framework's core contributions are: (1) it develops a novel capability-centric perspective to analyze a wide range of agentic AI systems; (2) it distills three primary sources of risk intrinsic to agentic AI systems - components, design, and capabilities; (3) it establishes a clear nexus between each risk source, specific materialized risks, and corresponding technical controls; and (4) it provides a structured and practical approach to help organizations implement the framework. This framework provides a robust and adaptable methodology for organizations to navigate the complexities of agentic AI, enabling rapid and effective innovation while ensuring the safe, secure, and responsible deployment of agentic AI systems. Our framework is open-sourced hrefhttps://govtech-responsibleai.github.io/agentic-risk-capability-framework/here.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 14 of its 27 references that are in the map. First posted on arXiv 22 Dec 2025.
Builds on
14 of 27 references.
Closest ancestors · 10
Same direction, newest first
- 2025Design Patterns for Securing LLM Agents against Prompt Injections10 Jun
- 2025TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems4 Jun
- 2025Towards Adaptive Categories: Dimensional Governance for Agentic AI16 May
- 2025Progent: Securing AI Agents with Privilege Control16 Apr
- 2025How to evaluate control measures for LLM agents? A trajectory from today to superintelligence7 Apr
- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
- 2025CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities21 Mar
- 2024RedCode: Risky Code Execution and Generation Benchmark for Code Agents12 Nov
- 2024AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents11 Oct
- 2023Identifying the Risks of LM Agents with an LM-Emulated Sandbox25 Sep
Other references · 4
- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
- 2023AI Control: Improving Safety Despite Intentional Subversioncited by 207
- 2024On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agentscited by 92
- 2024ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stagescited by 77
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026Bounded Agents: Delegation Security for Multi-Agent AI Systemscited by 6
- 2026Beyond the 'Diff': Addressing Agentic Entropy in Agentic Software Developmentcited by 1
- 2026Distributed Legal Infrastructure for a Trustworthy Agentic Webcited by 0
- 2026Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agentscited by 0
- 2026Bounded Agents: Delegation Security for Multi-Agent AI Systems16 Aug 2026
- 2026Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents9 Aug 2026
- 2026Distributed Legal Infrastructure for a Trustworthy Agentic Web6 Mar 2026
- 2026Beyond the 'Diff': Addressing Agentic Entropy in Agentic Software Development3 Mar 2026
Similar, not cited
Close in wording, no citation either way
- 2026TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems
- 2026Separating Capability from Permission: A Governance Framework for Agentic AI Autonomy Levels
- 2026AI-GRACE: A Use-Case Operationalization Framework for Agentic AI: From Organizational Objectives and Obligations to Deployment Capabilities and Architecture
- 2026Governing the Agentic Enterprise: A Governance Maturity Model for Managing AI Agent Sprawl in Business Operations
- 2025AGENTSAFE: A Unified Framework for Ethical Assurance and Governance in Agentic AI
- 2025A Safety and Security Framework for Real-World Agentic Systems
- 2026Risk Architecture for AI-Native Engineering Teams: An Organizational Framework for Agentic System Governance
- 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products