Designing Multi-layered Runtime Guardrails for Foundation Model Based Agents: Swiss Cheese Model for AI Safety by Design

• URL: http://arxiv.org/abs/2408.02205v3
• Date: Tue, 19 Nov 2024 01:10:07 GMT
• Title: Designing Multi-layered Runtime Guardrails for Foundation Model Based Agents: Swiss Cheese Model for AI Safety by Design
• Authors: Md Shamsujjoha, Qinghua Lu, Dehai Zhao, Liming Zhu,
• Abstract summary: Foundation Model (FM)-based agents are revolutionizing application development across various domains. We present a comprehensive taxonomy of runtime guardrails for FM-based agents to identify the key quality attributes for guardrails and design dimensions. Inspired by the Swiss Cheese Model, we also propose a reference architecture for designing multi-layered runtime guardrails for FM-based agents.
• Score: 12.593620173835415
• License:
• Abstract: Foundation Model (FM)-based agents are revolutionizing application development across various domains. However, their rapidly growing capabilities and autonomy have raised significant concerns about AI safety. Researchers are exploring better ways to design guardrails to ensure that the runtime behavior of FM-based agents remains within specific boundaries. Nevertheless, designing effective runtime guardrails is challenging due to the agents' autonomous and non-deterministic behavior. The involvement of multiple pipeline stages and agent artifacts, such as goals, plans, tools, at runtime further complicates these issues. Addressing these challenges at runtime requires multi-layered guardrails that operate effectively at various levels of the agent architecture. Thus, in this paper, we present a comprehensive taxonomy of runtime guardrails for FM-based agents to identify the key quality attributes for guardrails and design dimensions based on the results of a systematic literature review. Inspired by the Swiss Cheese Model, we also propose a reference architecture for designing multi-layered runtime guardrails for FM-based agents, which includes three dimensions: quality attributes, pipelines, and artifacts. The proposed taxonomy and reference architecture provide concrete and robust guidance for researchers and practitioners to build AI-safety-by-design from a software architecture perspective.

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