arXiv:2603.14987v2 Announce Type: replace Abstract: Agentic AI systems increasingly act through tool-augmented, multi-step workflows whose failures (unsafe tool use, unauthorised actions, social harm) carry deployment-level consequences. Evaluation practice remains fragmented across isolated benchmark slices, and "trustworthiness" is frequently invoked but rarely defined operationally. We argue the central limitation is twofold: (i) the absence of a measurable specification of what agent trustworthiness means, and (ii) the lack of a principled notion of representativeness allowing assessment o
The rapid deployment of agentic AI systems necessitates a robust and standardized evaluation framework for trustworthiness, which current benchmarks lack.
The operational definition and representative evaluation of 'trustworthiness' will directly influence the development, regulation, and adoption rate of autonomous AI systems with significant real-world implications.
The focus is shifting from isolated benchmark performance to comprehensive, operationally defined trustworthiness evaluations for agentic AI, impacting how these systems are designed and deployed.
- · AI safety researchers
- · developers of trustworthy AI evaluation tools
- · enterprises deploying agentic AI
- · AI developers ignoring trustworthiness
- · fragmented benchmark providers
- · consumers harmed by untrustworthy agents
Industry standards for agentic AI trustworthiness will emerge, guiding development and deployment.
Regulatory bodies will incorporate these trustworthiness standards into compliance frameworks, potentially slowing adoption for non-compliant systems.
Public trust and widespread adoption of agentic AI will accelerate in sectors where robust trustworthiness can be demonstrated.
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