智能体流程的黑盒：锚定区块链的AI智能体通信、人工监督与GRC审计证据
A Black Box for Agentic Processes: Blockchain-Anchored Evidence for AI Agent Communication, Human Oversight, and GRC Audits
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中文总结 AI 辅助
该论文针对AI智能体工作流的可审计性问题，提出一种锚定区块链的产品中立黑盒架构，用于智能体通信等的密码学承诺，为GRC审计提供证据基础，适用于相关监管场景。
中文摘要 AI 辅助
自主AI智能体越来越多地与其他智能体通信、调用工具、交换中间结果并请求人工批准。这些工作流产生了新的可审计性问题：组织必须重建发生了什么、何时发生、涉及哪个智能体或人员、应用了哪个控制或策略，以及记录是否被修改过。受2026年OpenAI/Hugging Face事件的启发，这篇定位与架构论文提出了一种与产品和供应商无关的智能体流程黑盒架构。该架构为选定的智能体通信、人在环批准、工具调用和流程工件创建锚定区块链的密码学承诺，同时不将敏感内容上链。我们定义了一种证据模型，将时间锚定和工件完整性与事件排序区分开来，捕获真实性、授权锚定和因果可追溯性；后两种属性需要额外的架构控制。随后我们讨论其在治理、风险与合规（GRC）中的实际用途，包括合规测试、基于风险的证据选择、监控证据流、事件重建，以及符合《欧盟人工智能法案》《网络与信息系统指令2》（NIS2）和《网络弹性法案》（CRA）的监管报告准备。这篇定位与架构论文未提供实证性能或安全性评估，该方法无法防止智能体不当行为或证明语义真实性，而是强化了后续验证关键流程轨迹的证据基础。
英文摘要
Autonomous AI agents increasingly communicate with other agents, invoke tools, exchange intermediate results, and request human approvals. These workflows create a new auditability problem: organizations must reconstruct what happened, when it happened, which agent or human was involved, which control or policy applied, and whether records were modified afterwards. Motivated by the 2026 OpenAI/Hugging Face incident, this position and architecture paper proposes a product- and vendor-neutral black-box architecture for agentic processes. The architecture creates blockchain-anchored cryptographic commitments for selected agent communications, human-in-the-loop approvals, tool calls, and process artifacts without placing sensitive content on-chain. We define an evidence model that distinguishes temporal anchoring and artifact integrity from event ordering, capture authenticity, authorized anchoring, and causal traceability. The latter properties require additional architectural controls. We then discuss practical use for Governance, Risk, and Compliance (GRC), including compliance testing, risk-based evidence selection, monitoring evidence streams, incident reconstruction, and regulatory reporting readiness under the EU AI Act, NIS2, and the Cyber Resilience Act (CRA). This position and architecture paper does not present an empirical performance or security evaluation. The approach does not prevent agent misbehavior or prove semantic truth. Rather, it strengthens the evidentiary basis for later verification of critical process traces.
