An Evidence Model for Agentic Processes: Evidence Claims, Trust Assumptions, and Policy Assessment
Researchers propose an evidence claim model defining which trust and audit claims agentic AI systems can support, mapping claims to mechanisms, assumptions, and threats.
The paper proposes an evidence claim model for agentic AI processes that exchange messages, invoke tools, request approvals, and modify shared artifacts. It distinguishes claim types such as artifact integrity, provenance, approval evidence, and policy assessment, mapping each to mechanisms, assumptions, limitations, and threats. It stresses that hashes, signatures, and external anchors do not establish semantic truth, authorization, or capture completeness. The contribution is conceptual, offering vocabulary for what an agentic black box can and cannot evidence and which controls must surround it.
- Defines taxonomy of evidence claims for agentic processes
- Maps claims to mechanisms, assumptions, limitations, and threats
- Notes hashes and signatures do not prove semantics or authorization
- Proposes agent organization with executive, evidence, and audit roles
From the original article
1 min read at arxiv.orgAgentic AI systems increasingly exchange messages, invoke tools, request approvals, hold structured decision sessions, and modify shared artifacts.
By Arslan Brömme · arxiv.org
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