# Nagare: A Human-Centered, Evidence-First AI Workflow Kanban System

> This article introduces how the Nagare project, guided by an evidence-first design philosophy, builds an AI workflow kanban system that supports human-AI collaboration and achieves seamless integration of human and AI workflows.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-24T06:15:27.000Z
- 最近活动: 2026-05-24T06:30:55.177Z
- 热度: 157.7
- 关键词: AI工作流, 看板系统, 人机协作, 证据优先, 项目管理, AI代理, 工作流自动化
- 页面链接: https://www.zingnex.cn/en/forum/thread/nagare-ai
- Canonical: https://www.zingnex.cn/forum/thread/nagare-ai
- Markdown 来源: floors_fallback

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## Nagare: Introduction to the Human-Centered, Evidence-First AI Workflow Kanban System

Nagare is an AI workflow kanban system developed by hachiware-labs. Its core is the **evidence-first** design philosophy, aiming to build workflows that support human-AI collaboration and achieve seamless integration between humans and AI. The project addresses the shortcomings of traditional tools in human-AI hybrid collaboration, improving work transparency, traceability, and collaboration efficiency. It is applicable to multiple scenarios such as content creation and software development.

## Background: New Tool Requirements for Human-AI Collaboration in the AI Era

With the rapid development of AI, human-AI hybrid collaboration has become the norm (e.g., AI generates drafts and humans review them). Traditional tools cannot track human and AI work or record decision-making basis. Nagare emerged as a solution; its name comes from the Japanese word "流" (nagare), symbolizing smooth work and natural connection between humans and AI.

## Core Philosophy: Three Principles of Evidence-First

1. **Traceable Decisions**: Task changes and decisions require clear evidence (AI reports, data results, etc.); 2. **Separation of Human and AI Contributions**: Distinguish between human and AI contributions to support quality improvement; 3. **Verifiable Progress**: Automatically update status by linking to actual outputs, avoiding manual update issues.

## System Architecture and Core Features

- **Kanban View**: Multi-dimensional classification, real-time synchronization, evidence display, human-AI identification;
- **AI Agent Integration**: Registration management, task assignment, result verification, feedback learning;
- **Evidence Management**: Collection, association, display, archiving;
- **Workflow Orchestration**: Conditional branching, human-AI handover, parallel processing, exception handling.

## Typical Application Scenarios: Collaborative Processes Across Multiple Domains

1. **Content Creation**: AI draft → Evidence collection → Human review → Iterative optimization → Quality verification → Publication;
2. **Software Development**: Requirement analysis → Code generation → Review → Testing → Deployment decision;
3. **Data Analysis**: Data exploration → Hypothesis proposal → Verification → Interpretation → Report generation;
4. **Customer Service**: Ticket classification → Knowledge retrieval → Response generation → Review → Satisfaction tracking.

## Technical Implementation: Synergy with Existing Toolchains and Extensibility

- **Tool Integration**: Integrates with Git, CI/CD (Jenkins), document systems (Notion), and communication tools (Slack);
- **Data Model**: Designed around tasks, evidence, participants, workflows, and decisions;
- **Extensibility**: Plugin architecture, API-first approach, multi-tenant support.

## Limitations and Improvement Directions

- **Evidence Collection Completeness**: Relies on tool integration; improvement directions include flexible import, manual supplementation, and unstructured data processing;
- **AI Agent Generality**: Requires targeted configuration; improvement directions include standardized interfaces, automatic matching, and benchmark testing;
- **Learning Curve**: Team adaptation cost; improvement directions include template guides, gradual introduction, and training materials.

## Summary and Outlook: A New Paradigm for Human-AI Collaboration in the AI Era

Nagare proposes a new paradigm for workflow management in the AI era. By prioritizing evidence, it addresses transparency and traceability issues in human-AI collaboration and provides a data foundation for continuous AI improvement. In the future, such tools will become infrastructure for the co-evolution of humans and AI, offering a reference framework for AI-assisted teams.