代理AI系统自动化企业工作流，但现有防御措施——护栏、语义过滤器——是概率性的，经常被绕过。我们引入了认证工作流，这是首个完整的信任层，用于企业代理AI。安全归结为保护四个基本边界：提示、工具、数据和上下文。我们在每个边界跨越时强制执行意图（操作符合组织政策）和完整性（操作具有密码学认证），结合密码学消除攻击类别与运行时策略执行。这提供了确定性的安全——操作要么携带有效的密码学证明，要么被拒绝。我们引入了MAPL，一种针对AI的策略语言，动态表达代理约束，随着代理演变和调用上下文变化而扩展，通过分层组合与密码学认证的工作流依赖关系，将政策规模从O(log M + N)与O(M x N)规则相比。我们通过整合九个领先框架（MCP、A2A、OpenAI、Claude、LangChain、CrewAI、AutoGen、LlamaIndex、Haystack）的通用安全运行时，通过薄适配器实现实用性，无需任何协议修改。形式证明确立了完整性和正确性。实证验证显示，在174个测试用例中实现了100%召回率，零误报，保护了OWASP Top 10风险中的9个，完全缓解了两个高影响生产CVE。
英文摘要：
Agentic AI systems automate enterprise workflows but existing defenses--guardrails, semantic filters--are probabilistic and routinely bypassed. We introduce authenticated workflows, the first complete trust layer for enterprise agentic AI. Security reduces to protecting four fundamental boundaries: prompts, tools, data, and context. We enforce intent (operations satisfy organizational policies) and integrity (operations are cryptographically authentic) at every boundary crossing, combining cryptographic elimination of attack classes with runtime policy enforcement. This delivers deterministic security--operations either carry valid cryptographic proof or are rejected. We introduce MAPL, an AI-native policy language that expresses agentic constraints dynamically as agents evolve and invocation context changes, scaling as O(log M + N) policies versus O(M x N) rules through hierarchical composition with cryptographic attestations for workflow dependencies. We prove practicality through a universal security runtime integrating nine leading frameworks (MCP, A2A, OpenAI, Claude, LangChain, CrewAI, AutoGen, LlamaIndex, Haystack) through thin adapters requiring zero protocol modifications. Formal proofs establish completeness and soundness. Empirical validation shows 100% recall with zero false positives across 174 test cases, protection against 9 of 10 OWASP Top 10 risks, and complete mitigation of two high impact production CVEs.
Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflow
Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, USA（电气与计算机工程系，工程学院，纽约大学，布鲁克林，纽约，美国）
Composing Verifiable Conceptual Models via Building Blocks: Towards Design-Time Verification of Agentic AI Workflows
Team EVERGREEN Inria Centre Inria d’Université Côte d’Azur（法国国家信息与自动化研究所蔚蓝海岸大学中心EVERGREEN团队）；Department of Data Science William & Mary（威廉与玛丽学院数据科学系）；Office of Enterprise Research and Innovation Old Dominion University（欧道明大学企业研究与创新办公室）
Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation
Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory（太平洋西北国家实验室物理与计算科学部门）；Department of Chemical Engineering, University of Washington（华盛顿大学化学工程系）
University of Sydney（悉尼大学）；University of Adelaide（阿德莱德大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）
A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows
Old Dominion University（旧 Dominion 大学）；Florida International University（佛罗里达国际大学）；Nanyang Technological University（南洋理工大学）；University of Colombo（科伦坡大学）
A Sober Look at Agentic Misalignment in Automated Workflows
University of Virginia（弗吉尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Utah（犹他大学）；University of Notre Dame（圣约翰大学）；George Washington University（乔治华盛顿大学）
Learning to Compose for Cross-domain Agentic Workflow Generation
Hong Kong University of Science and Technology（香港理工大学）；Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；Southeast University（东南大学）；University of Tsukuba（茨口大学）
Canadian University Dubai（迪拜加拿大大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Zayed University（扎耶德大学）；Hong Kong Baptist University（香港浸会大学）；University of Technology Sydney（悉尼科技大学）
Plausible but Wrong: A case study on Agentic Failures in Astrophysical Workflows
Bonn-Aachen International Center for Information Technology, University of Bonn, Germany（波恩-阿森国际信息科技中心，波恩大学，德国）；Lamarr Institute for Machine Learning and Artificial Intelligence, Germany（拉马尔机器学习与人工智能研究所，德国）
HyEvo: Self-Evolving Hybrid Agentic Workflows for Efficient Reasoning
East China Normal University（华东师范大学）；Beihang University（北京航空航天大学）；Shanghai University of International Business and Economics（上海对外经贸大学）；Fudan University（复旦大学）
