Agentic AI systems plan, invoke tools, and act with limited supervision; they are now both the subject of audits and, increasingly, the auditor. Independence-the foundation of assurance-is still applied to them as a binary. We argue that it must be graded along three orthogonal axes: principal independence (who controls the auditor), substrate independence (an auditor that shares the auditee's foundation-model family, toolchain, or guardrails fails with the auditee), and evidence independence (whether evidence is attestable rather than self-reported). Each axis has precedent; the contribution is to grade all three on a single audit, aggregate them by the weakest link, and apply the same rubric when the auditor is itself an agent. We give the model a formal basis by transplanting the beta-factor model of common-cause failure from reliability engineering. We also provide a seven-step protocol whose outputs a third party can verify, a structural detectability analysis of a procurement-controls agent audited at three grades, and a closed-form study of the model in which, at a declared common-mode share of 0.90 for a same-family auditor and a declared reporting probability of 0.6 for an internal team, a conventional internal audit of an agent-a real audit team, a second agent, and provider logs-surfaces 5.9% of the faults it could in principle see and, independently of those two values, none at all in half of the fault classes. Because an evidence grade says who could have altered a record, not what the record covers, every load-bearing record also carries a declared-scope predicate, and reports carry a false-flag rate beside the detection floor wherever the auditor's own performance has been measured. We map the triple to the EU AI Act as amended, ISO-IEC 42006, UK public-sector risk-management guidance, and audit-regulator practice.

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