arXiv:2603.10044v2 Announce Type: replace-cross Abstract: A safety score earned on a benchmark need not predict how the same model behaves once it is wrapped in an agentic scaffold the benchmark never tested. We ran six frontier models through four deployment configurations (direct API, ReAct, multi-agent critic, map-reduce delegation): N = 62,808 blinded, pre-registered, equivalence-tested evaluations across four safety benchmarks (BBQ, TruthfulQA, XSTest/OR-Bench, sycophancy), plus three supporting analyses. ReAct and multi-agent scaffolds stay within a pre-registered +/-2 pp equivalence mar
The rapid deployment of advanced AI models alongside the increasing focus on AI safety necessitates a deeper understanding of how real-world deployment conditions affect safety metrics, moving beyond pure benchmark performance.
This research provides critical insights into the discrepancy between benchmarked AI safety and actual performance in complex, agentic environments, highlighting the need for more robust evaluation methods for AI systems in production.
The understanding that AI safety is not solely an intrinsic model property but is significantly modulated by deployment architectures, challenging current evaluation paradigms and prompting new approaches to safe AI development.
- · AI safety researchers
- · Developers of agentic AI systems
- · Organizations prioritizing robust AI deployment
- · AI ethics and governance bodies
- · AI models without robust scaffolding
- · Developers relying solely on traditional benchmarks
- · Organizations with naive AI deployment strategies
AI safety evaluations will need to integrate deployment scaffold analysis as a standard component.
The development of agentic AI systems will increasingly incorporate safety-enhancing architectures like multi-agent critics from the outset.
Regulatory frameworks for AI will begin to differentiate safety requirements based on deployment context, not just inherent model capabilities.
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