Published on May 8, 2026
Organizations increasingly rely on enterprise agents to navigate complex, policy-constrained environments. These systems operate under strict access controls, often delivering answers that seem complete. However, crucial evidence can remain outside users’ authorization boundaries.
The introduction of Partial Evidence Bench marks a significant shift in evaluating these systems. This new tool measures failures in completeness awareness through various scenarios, including due diligence and compliance audits. It includes 72 tasks that illustrate how systems can appear correct while overlooking critical information.
Initial findings indicate that silent filtering poses significant risks, while adopting explicit fail-and-report mechanisms can enhance safety. The benchmark allows for evaluations along multiple dimensions, such as answer quality and completeness awareness, without needing human oversight. This innovation highlights systemic issues previously obscured.
The implications are profound for enterprises relying on automated decision-making. agentic systems handle incomplete information, organizations can better understand and mitigate risks. This tool not only aids in governance but marks a pivotal step in ensuring accountability in AI-driven environments.
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