Beyond Task Success: An Evidence-Synthesis Framework for Evaluating, Governing, and Orchestrating Agentic AI

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 g

Categories

@misc{koch2026beyond,
  title = {{Beyond Task Success: An Evidence-Synthesis Framework for Evaluating, Governing, and Orchestrating Agentic AI}},
  author = {Christopher Koch and Joshua Andreas Wellbrock},
  year = {2026},
  month = apr,
  eprint = {2604.19818},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2604.19818}
}