# Governed Workflow: A Zero-Trust Governance Framework for Claude Code > Governed Workflow is a zero-trust orchestration layer designed for Claude Code. It ensures the controllability and security of AI-assisted coding sessions through phase gating, scope locking, and manual approval mechanisms. - 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm) - 发布时间: 2026-04-24T20:45:09.000Z - 最近活动: 2026-04-24T20:50:19.935Z - 热度: 150.9 - 关键词: Claude Code, AI编程, 工作流治理, 零信任, MCP服务器, 代码审查, 人工审批, 软件开发 - 页面链接: https://www.zingnex.cn/en/forum/thread/governed-workflow-claude-code - Canonical: https://www.zingnex.cn/forum/thread/governed-workflow-claude-code - Markdown 来源: floors_fallback --- ## Governed Workflow: Introduction to the Zero-Trust Governance Framework for Claude Code Governed Workflow is a zero-trust orchestration layer designed for Claude Code. It addresses issues such as lack of structured processes, scope creep, self-certification, and context loss in traditional AI coding assistants for complex projects through phase gating, scope locking, and manual approval mechanisms, ensuring the controllability and security of AI-assisted coding sessions. The framework introduces software engineering best practices into the field of AI-assisted coding and is suitable for scenarios like enterprise-level AI programming and critical system development. ## Governance Challenges in AI Programming Traditional AI coding assistants use a 'request-response' model, which has the following flaws in complex projects: 1. **Lack of structured processes**: AI may code directly without sufficient research, leading to unreasonable solution design; 2. **Scope creep risk**: Modifying files beyond expectations in a single session, introducing side effects; 3. **Self-certification issue**: AI tends to believe its own code is correct, lacking external validation; 4. **Context loss**: Compression of long session context leads to loss of important information, and AI "forgets" previous decisions. Governed Workflow aims to address these issues. ## Core Design Concepts: Zero Trust and Phased Workflow ### Zero Trust Principles Adopting the 'never trust, always verify' concept: AI agents cannot self-certify their results; each phase transition requires server verification; research must be well-documented; file modifications are limited to authorized scopes; and key checkpoints require manual approval. ### Phased Workflow Divided into six phases: Evaluation, Research, Planning, Execution, Review, and Delivery. Each phase has clear entry conditions and exit criteria. ### Manual Gating Mechanism Manual gates are set at four key transition points: Preparation Review (1.4), Plan Review (2.1), Code Review (3.N.3), and Final Approval (4.2). Continuation requires manual approval via the management panel to prevent AI from self-approving critical decisions. ## Detailed Explanation of the Governed Workflow ### Phase 1: Evaluation and Research Starting with evaluation: AI proposes research questions to clarify scope and objectives. In the research phase, questions need to be answered and evidence collected. The **research proof mechanism** requires findings to be accompanied by typed proofs (code location, network source, code differences), which are verified by the Prover agent. Unverified claims cannot pass. ### Phase 2: Planning Develop a detailed execution plan including architecture diagrams, task decomposition, and acceptance criteria. Execution tasks are divided into multiple sub-phases, defining the scope of files that must be modified (must) and those that may be modified (may). Acceptance criteria are accepted/rejected by the user, and server verifies test-related criteria upon submission. ### Phase 3: Execution Iterative loop mode: Each sub-phase includes implementation, verification, repair, code review, and submission steps. Production and test code are written by different agents. The **scope locking mechanism** enforces file scope constraints; updating the scope or plan requires re-approval. **Verification configuration files** run automatic code quality checks. ### Phase 4: Review and Delivery Blind review phase (4.0): The reviewer agent does not know the implementation details. Issue handling phase (4.1): Resolve identified issues. Final approval (4.2): Deliver after user confirmation. The **review system** centrally manages feedback, and ReviewGuard blocks phase progression until all review items are resolved by the user. ## Technical Implementation Highlights ### MCP Server Integration Integrated with Claude Code via MCP server, providing a standardized tool call interface. Agents call workspace_advance to advance the workflow. ### Flask Management Panel A visual interface where users can view phases, approval requests, manage acceptance criteria, configure verification files, etc. Encrypted random numbers ensure approval security. ### Session Recovery Mechanism Records phase operations, obstacles, decisions, and file changes via progress entries. Restarts regenerate agents to ensure continuity of long-cycle projects. ### Telegram Remote Control Supports remote session control via Telegram bot. Multiple sessions share the bot, with prefixes identifying workspaces. An orphan detection mechanism automatically recovers dead sessions. ### Modular Architecture Functions are organized as modules. The management panel scans directories to enable/disable modules; currently includes the Telegram module. ## Agent Role Division and Security Boundaries ### Agent Role Division The framework defines 16 professional agents, coordinated by the Orchestrator: - Plan Advisor: A persistent teammate throughout the session; - Researcher: Responsible for research and answering questions; - Prover: Verifies research findings and proofs; - Engineer: Writes production code; - Test Engineer: Writes test code; - Reviewer: Conducts blind code reviews; - Orchestrator: Coordinates all agents. ### Security Boundaries and Limitations A local workflow governance layer aimed at preventing accidental or overconfident agents from bypassing controls; not designed to defend against malicious users, compromised machines, or hostile networks. Security assumptions: Local machine is trusted; user controls the management panel; network mode is behind a trusted LAN/secure tunnel; port 5111 is not exposed to the public; management token grants full control. ## Application Scenarios and Value ### Application Scenarios - **Enterprise-level AI programming**: Ensures process traceability and auditability in compliance scenarios; - **Critical system development**: Multi-phase verification and manual gating reduce defect risks; - **Team collaboration**: Structured processes coordinate multiple developers sharing AI assistants to avoid conflicts; - **Newcomer training**: Standardized workflows help novices learn best practices. ### Value Represents the development of AI-assisted programming towards maturity and controllability, balancing the release of AI potential with human supervision and control. It is a topic of ongoing interest in the field of software engineering.