🤖 AI Summary
This work addresses the limitations of existing evaluation methods, which focus narrowly on task completion and fail to ensure trustworthy deployment of embodied agents in multi-step, externally impactful scenarios, while also lacking coordination among evaluation, governance, orchestration, and runtime assurance. To bridge this gap, the paper proposes an integrated four-layer framework that establishes, for the first time, a closed-loop mechanism linking governance obligations to verifiable execution. Guided by the ODTA principles—Observability, Decidability, Timeliness, and Attestability—the framework introduces runtime localization testing and minimal action evidence bundles. Through a human-in-the-loop evidence synthesis approach, it formally connects policy requirements to concrete agent behaviors, exposing critical gaps such as the inability of static permissions and prompts to govern path-dependent actions. Validation via an enterprise procurement agent demonstrates the framework’s capacity to unify safety, robustness, and trajectory-level evaluation.
📝 Abstract
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.