deployment policy design

Enterprise-scale general-purpose agents lack built-in, reusable governance mechanisms for autonomous cross-tool operation, making it difficult to satisfy requirements for compliance, auditability, and behavioral controllability. This work proposes the CUGA policy system, which embeds runtime governance capabilities into five critical checkpoints of the agent execution pipeline—intent protection, playbook guidance, tool invocation control, human approval gating, and output formatting—through a modular “policy-as-code” architecture. Without requiring model fine-tuning, CUGA enables proactive, continuous, and structured behavior control. By integrating typed governance primitives, dynamic playbook injection, and human-in-the-loop approval, the system effectively blocks malicious requests, enforces structured tool sequences, and triggers manual review for high-risk operations in healthcare scenarios, significantly enhancing policy adherence, execution consistency, and deployment safety.

Large language model (LLM) agents exhibit unreliable adherence to corporate policies in business process automation. To address this, we propose a deterministic, transparent, and modular policy compliance framework comprising two phases: (1) an offline phase that compiles natural-language policy documents into verifiable guard code, and (2) a runtime phase that inserts lightweight, policy-agnostic guards before tool invocation—thereby decoupling policy enforcement from agent logic. This design enhances interpretability, maintainability, and agility in policy updates. Experiments on the τ-bench Airlines testbed demonstrate the framework’s effectiveness in intercepting policy-violating actions, validating its feasibility. However, empirical evaluation also uncovers critical deployment challenges, including incompleteness in policy coverage and difficulties in dynamically adapting guards to contextual changes. The framework thus advances policy-aware LLM agent deployment while surfacing key open issues for future work.

This study addresses environment inconsistencies, expanded supply chain attack surfaces, and weak compliance arising from redundant builds in cloud deployments by proposing an artifact promotion control model that establishes a “build once” principle. Methodologically, the work rigorously distinguishes artifact from environment identities, demonstrates that secret injection compromises artifact integrity, derives that release roles require no production credentials, and implements end-to-end autonomous governance on AWS in accordance with NIST SP 800-204D. Experimental results show that the model supports fully autonomous releases via a single command, completing 22 deployments in the first month with individual rollbacks requiring only 36 seconds. These findings indicate a significant reduction in operational complexity alongside strengthened integrity guarantees for cloud deployment pipelines.

本文提出Agile-V Assurance Spine，通过权威源配置文件、工件绑定、风险适当独立性和时效性等方法解决工程生命周期中对代理输出的正当行动问题。

本文提出一种模型，通过设计科学研究方法解决在软件工程中选择大型语言模型时面临的治理与合规难题，采用多层结构和评估协议以增强决策过程中的合规性。

This work addresses the semantic gap faced by current AI agents in enforcing natural language policies: the intended policy semantics are difficult to enforce precisely and interpretably at the system level. To bridge this gap, the authors propose a novel approach that integrates agent-side context with kernel-level enforcement mechanisms. For the first time, policy context is preserved on the agent side, while a domain-specific language (DSL) for information flow control (IFC), implemented via eBPF, enables comprehensive, action-level policy enforcement within the operating system kernel. This framework supports cross-event data-flow and ordering constraints, significantly improving policy compliance rates by covering indirect execution paths invisible to conventional tool-call interception. The system incurs only 1.9%–8.4% runtime overhead and provides semantically clear feedback instead of ambiguous errors.

This work addresses the inadequacy of existing large language model (LLM) lifecycle frameworks, which predominantly emphasize operational efficiency while lacking explicit support for security-critical activities—such as data provenance, component signing, and access control—and failing to align governance requirements with specific lifecycle phases. The paper proposes the first security-oriented LLM system lifecycle model, structured not by workflow but by security boundaries, organizing 32 phases into four layered pipelines: data, model, distribution, and application, while integrating LLMOps and governance pillars. It uniquely identifies 13 distinct security-critical phases and exposes a structural imbalance wherein regulatory evidence is concentrated at deployment despite pivotal decisions occurring during development. By mapping key standards—including NIST AI RMF, the EU AI Act, and ISO/IEC 42001—the study establishes a phase-to-governance correspondence mechanism, yielding a comprehensive, lifecycle-spanning security analysis framework that offers structured guidance for compliance and secure design.

This study addresses the challenges of collaboration and quality control in open-source deep learning projects stemming from inadequate governance mechanisms. Drawing on the Institutional Analysis and Development (IAD) framework, it employs a mixed-methods empirical approach combining document content analysis and code commit tracking across PyTorch, TensorFlow, and PaddlePaddle. The analysis encompasses 109 governance documents and over 1,700 code commits, systematically uncovering the structure, temporal evolution, and functional dimensions of governance rules. The research identifies 17 rule themes and 7 rule types, revealing a distinct evolutionary pattern wherein operational rules emerge early and undergo frequent revisions, while structural rules appear later and evolve more steadily. Four core governance functions are distilled, culminating in 33 actionable recommendations for effective open-source AI project governance.

本文提出PolicyGuide方法，通过将策略编译为工作流图并使用主动验证器来指导客服LLM代理遵循多步骤程序，从而提高策略合规性。

本文提出了一种基于LLM的系统，通过结构化分解和模式验证方法将自然语言访问控制策略转换为可执行代码，以提高生成策略的可靠性和安全性。