Correct Is Not Governed: Provenance Integrity in Agentic Workflows
Paper argues that correct AI agent outcomes are insufficient for institutional trust — provenance integrity covering authority, evidence, and freshness is essential, particularly for auditable regulated workflows.
Summary written by editorial AI · Source link below
arXiv:2608.12761v1 Announce Type: cross Abstract: Agentic workflows are commonly evaluated by whether they reach the correct outcome. That is insufficient in institutional settings, where a correct action may rely on the wrong authority, an unsupported completion claim, or work made stale by a later change. We define governed execution as work whose decisions, completion, and response to change are supported by inspectable provenance. We present Matrix, a deterministic causal-state layer that r
Editorial Analysis
Regulated enterprises deploying agentic AI must ensure governance frameworks verify not just outcome correctness but also authority chains and data provenance to satisfy audit demands.
Embed provenance-integrity checks into any agentic AI workflow before deploying it in audit-relevant business processes.
AI agents producing correct results may still fail governance tests — provenance and authority tracking is essential for regulated environments.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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