Who Can Evaluate AI Independently? A Framework for Analysing Evaluation Infrastructure

Agentic AI systems plan, use tools, maintain state, and act across multi-step workflows with external effects, meaning trustworthy deployment can no longer be judged by task completion alone. The current literature remains fragmented across benchmark-centered evaluation, standards-based governance, orchestration architectures, and runtime assurance mechanisms. This paper contributes a bounded evidence synthesis across a manually coded corpus of twenty-four recent sources. The core finding is a governance-to-action closure gap: evaluation tells us whether outcomes were good, governance defines what should be allowed, but neither identifies where obligations bind to concrete actions or how compliance can later be proven. To close that gap, the paper introduces three linked artifacts: (1) a four-layer framework spanning evaluation, governance, orchestration, and assurance; (2) an ODTA runtime-placement test based on observability, decidability, timeliness, and attestability; and (3) a minimum action-evidence bundle for state-changing actions. Across sources, evaluation papers identify safety, robustness, and trajectory-level measurement as open gaps; governance frameworks define obligations but omit execution-time control logic; orchestration research positions the control plane as the locus of policy mediation, identity, and telemetry; runtime-governance work shows path-dependent behavior cannot be governed through prompts or static permissions alone; and action-safety studies show text alignment does not reliably transfer to tool actions. A worked enterprise procurement-agent scenario illustrates how these artifacts consolidate existing evidence without introducing new experimental data.

The rapid deployment of autonomous AI agents across consequential domains including healthcare, legal services, nancial management, and enterprise workows has created an urgent governance challenge. While the eld of AI evaluation has produced sophisticated benchmarks for measuring agent capability, these frameworks measure exclusively what agents can do rather than how they do it. The ethical dimensions most critical to responsible agent deployment, in- cluding respect for human autonomy, harm avoidance, transparency and non-deception, privacy compliance, and accountability, are systematically absent from current evaluation practice. This paper addresses that gap through three contributions. First, a structured literature review of twelve leading AI agent benchmarks characterizes the ethical dimensions those bench- marks do and do not measure. Second, the Ethical Evaluation Framework for AI Agents (EEFA) is introduced as a comprehensive, operationalizable instrument for ethical agent evaluation, grounded in established AI ethics principles and designed for use by developers, auditors, and regulators. Third, a reproducible validation protocol is specied for applying the EEFA to deployed agent systems, with illustrative test scenarios across all ve framework dimensions. The EEFA is organized across ve primary dimensions: Autonomy Preservation, Harm Avoid- ance, Transparency and Non-Deception, Privacy Compliance, and Accountability and Traceabil- ity. The framework maps directly onto the requirements of the EU AI Act and the NIST AI Risk Management Framework, positioning it as a practical compliance assessment instrument for high-risk AI applications. Empirical application of the framework across diverse benchmarks and agent systems is identied as a priority direction for future work.

The most capable general-purpose AI (GPAI) models are mostly built in two jurisdictions, the United States and China, but the risks they carry land globally. Regionally advanced economies hosting no frontier developer, which we call AI middle-powers, are writing their own rules to govern GPAI. This paper investigates which GPAI-relevant provisions these AI middle-powers have enacted, mapping twenty jurisdictions including the European Union at the level of the individual provision, across four governance areas that trace the accountability chain for the model layer: systemic risk assessment, evaluation and verification, prohibitions with monitoring and detection, and serious incident reporting. Confirmed absence is recorded as data alongside positive provision. We find that jurisdictions converge on form, but diverge on force. Sixteen engage in at least three of the four governance areas, yet only about one in five provisions sit in binding law, and three-quarters of the instruments that do bind do so without defining GPAI. The institutional infrastructure shows the same shape: four in five of the mapped governance actors hold mandates that predate GPAI, and obligations attach wherever the inherited regime already reached, which is the application layer rather than the model. Where these states engage the model layer, they build capacity to observe it rather than impose duties on those who build it, and almost every evaluation body was constituted without the power to act on what it finds. Nominal coverage of the full accountability chain reaches eleven jurisdictions, but only five hold more than one provision in every area and, outside the EU, no jurisdiction imposes a binding evaluation duty on a model developer. The dataset gives researchers and policymakers a provision-level basis for identifying where regimes could align, and where coordination would have to start from scratch.