Verification Independence for Agentic AI: Designing Review Beyond Self-Correction and Multi-Agent Agreement

As agentic artificial intelligence systems increasingly produce, evaluate, and act on their own outputs, review architectures face a structural problem: adding a second judgment does not necessarily create an independent basis for judgment. This paper proposes verification independence as a task-level design property for agentic AI. Verification independence asks whether the review process is sufficiently separated from the production process to surface errors that the producer is systematically unlikely to detect. The framework distinguishes four dimensions of independence: framing, evidence, mechanism, and authority. It also formalizes a four-function review architecture consisting of a producer, challenger, evidence checker, and acceptance owner. The framework is positioned relative to research on LLM self-correction, chain-of-verification, multi-agent debate, LLM-as-a-judge, correlated model errors, and emerging agentic verification systems. Existing work shows both the value and limits of additional model-based review: structured verification can improve outputs, while multi-agent agreement, self-critique, and LLM judging remain vulnerable to shared assumptions, correlated errors, evaluator bias, and noisy rubric verification. The contribution here is integrative rather than a claim that any individual verification technique is new. It treats independence as a property of the relationship between production and review, not as a synonym for reviewer count or model diversity. The central claim is that a reviewer can provide another judgment without providing another independent basis for judgment. For consequential agentic workflows, reliable verification therefore requires deliberate separation of review roles and, where feasible, external evidence, deterministic tests, independent tools, or distinct authority boundaries. The paper further introduces a qualitative independence checklist, risk-adjusted verification depth, and safeguards against erosion of independence in long-horizon workflows.