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Norma: Autonomous Agent Workflow Orchestrator Based on PDCA Cycle

An intelligent agent orchestration tool written in Go, which implements automated workflow management for task planning, execution, verification, and submission through the Plan-Do-Check-Act cycle.

AI代理工作流编排PDCAGo语言Git工作流BeadsACP协议自动化任务管理
Published 2026-04-10 00:42Recent activity 2026-04-10 00:46Estimated read 4 min
Norma: Autonomous Agent Workflow Orchestrator Based on PDCA Cycle
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Section 01

Introduction / Main Floor: Norma: Autonomous Agent Workflow Orchestrator Based on PDCA Cycle

An intelligent agent orchestration tool written in Go, which implements automated workflow management for task planning, execution, verification, and submission through the Plan-Do-Check-Act cycle.

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Section 02

Background: Challenges in AI Agent Orchestration

With the rapid development of AI coding assistants and autonomous agents, how to effectively manage and orchestrate the workflows of these agents has become a new technical challenge. Existing solutions often have the following problems: messy working directories, difficulty in tracking task status, unpredictable agent execution results, and lack of standardized verification and submission processes.

Development teams need a tool that ensures transparent, reliable, and recoverable agent work—not only to manage agent execution but also to ensure that every change is properly isolated, verified, and recorded.

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Section 03

Introduction to Norma

Norma is an autonomous agent workflow orchestrator developed by the NormaHQ team and written in Go. It bridges the gap between high-level task management and low-level code execution through a strict Plan → Do → Check → Act (PDCA) cycle.

Norma's design philosophy emphasizes transparency and reliability: every agent operation is recorded, every change is isolated in a Git worktree, and the entire runtime state is directly persisted to the task backlog. This design makes agent workflows auditable, recoverable, and collaborative.

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Section 04

Core Design Philosophy: PDCA Cycle

Norma's core is the classic PDCA quality management cycle, applied to the workflow orchestration of AI agents:

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Section 05

Plan (Planning)

Refine high-level goals into specific work plans and acceptance criteria. Norma analyzes the task description, generates a structured execution plan, and clarifies the expected output of each step.

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Section 06

Do (Execution)

Agents execute the plan in an isolated workspace. Norma creates an independent Git worktree for each task to ensure that agent modifications do not pollute the main working directory.

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Section 07

Check (Verification)

Evaluate the content of the workspace against the acceptance criteria and generate a PASS/FAIL verdict. This step ensures that only changes that meet quality standards can proceed to the next stage.

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Section 08

Act (Action)

If the check passes, Norma automatically merges and commits the changes to the main branch using the Conventional Commits specification; if it fails, the cycle continues or preparation for re-planning is made.