Beyond single-channel agentic benchmarking
This reframes safety evaluation for AI agents in human-AI systems, addressing a critical gap in benchmarking for safety-critical applications, though it is incremental in applying established engineering principles to AI.
The paper argues that current benchmarks for AI agent safety, which focus on isolated task accuracy, mischaracterize operational safety in human-in-the-loop environments, and demonstrates that imperfect AI systems can provide significant safety utility by acting as redundant audit layers against human errors like vigilance decrement.
Contemporary benchmarks for agentic artificial intelligence (AI) frequently evaluate safety through isolated task-level accuracy thresholds, implicitly treating autonomous systems as single points of failure. This single-channel paradigm diverges from established principles in safety-critical engineering, where risk mitigation is achieved through redundancy, diversity of error modes, and joint system reliability. This paper argues that evaluating AI agents in isolation systematically mischaracterizes their operational safety when deployed within human-in-the-loop environments. Using a recent laboratory safety benchmark as a case study demonstrates that even imperfect AI systems can nonetheless provide substantial safety utility by functioning as redundant audit layers against well-documented sources of human failure, including vigilance decrement, inattentional blindness, and normalization of deviance. This perspective reframes agentic safety evaluation around the reliability of the human-AI dyad rather than absolute agent accuracy, with a particular emphasis on uncorrelated error modes as the primary determinant of risk reduction. Such a shift aligns AI benchmarking with established practices in other safety-critical domains and offers a path toward more ecologically valid safety assessments.