AWS 开源 AI-DLC：跨 Claude Code 与 Cursor 的自适应工作流引擎，统一多智能体编码规范

原标题：Aidlc: Adaptive workflow steering rules for AI coding agents

68AI 研判分

核心综述

AWS Labs 发布开源项目 AI-DLC，旨在将分散的 AI 编程助手转化为结构化、可验证的软件交付生命周期。该工具采用“单一核心、多宿主”架构，原生支持 Claude Code、Cursor、GitHub Copilot 等主流环境，通过自适应规则引导智能体执行标准化开发流程。

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本文目录7 个章节

AI-DLC - one core, many harnesses

AI-DLC (AI-Driven Development Life Cycle) turns AI coding assistants into structured, verifiable software-delivery workflows. One harness-neutral core runs natively in Claude Code, Kiro CLI, Kiro IDE, Codex CLI, Cursor, opencode, and GitHub Copilot.

The Quick Start below installs the latest stable AI-DLC release.

Quick Start

1. Install AI-DLC

macOS, Linux, or WSL:

BASH

curl -fsSL https://github.com/awslabs/aidlc-workflows/releases/latest/download/install.sh | sh

Windows PowerShell:

POWERSHELL

irm https://github.com/awslabs/aidlc-workflows/releases/latest/download/install.ps1 | iex

The installer sets up everything AI-DLC needs: the native aidlc command and every harness runtime. You do not need to install Bun or Node.js; a native install runs its hooks and commands through aidlc and does not call bun. On Windows, it installs for the current account and automatically registers the bin directory in User PATH. Run it from a normal PowerShell window; one opened with "Run as administrator" gets a warning and a prompt, since installing as administrator is less safe. Open a new terminal if another session cannot find aidlc. To skip both persistent and current-process PATH changes, use -NoModifyPath. Windows uninstall removes only the User PATH entry recorded as installer-owned. On macOS, Linux, or WSL, follow the installer's PATH instruction if needed.

Prefer to manage the project files yourself, or cannot install a native executable? The manual-copy path is the one place Bun is needed: install Bun, download aidlc-copy-runtime-X.Y.Z.tar.gz from the release, and copy the complete runtime/<harness>/ directory into your project. This path does not require the native aidlc command.

2. Configure a project

From the project root, select the harness you use:

BASH

cd /path/to/your-project
aidlc config --harness claude
aidlc doctor

Replace claude with kiro, kiro-ide, codex, cursor, opencode, or copilot. Running aidlc config without --harness starts the interactive setup when a terminal is available. If you use Kiro IDE's own terminal in a project folder you have not trusted yet, Kiro first asks whether you trust it. Choose Trust Folder & Continue only for your own project or one you have checked, because trusting lets the folder's .kiro hooks run commands on your machine; otherwise choose Cancel and review the folder first (see First run).

3. Start a workflow

Open your harness in the configured project and describe the work:

TEXT

/aidlc Build a REST API for inventory management

Codex CLI uses $aidlc instead of /aidlc. In Kiro IDE, first choose aidlc in the chat panel's agent picker. AI-DLC selects a workflow from the request, asks for missing decisions, and stops at approval gates before moving forward.

For provider setup, trust prompts, and harness-specific prerequisites, use the guide in the table below. The complete walkthrough is in Getting Started.

Pick your harness

Harness	Configure	Open	Invoke	Guide
Claude Code	`aidlc config --harness claude`	`claude`	`/aidlc`	Getting Started
Kiro IDE >= 1.1.70 / Kiro CLI >= 2.24.1 (v3)	`aidlc config --harness kiro-ide`	Open the project in Kiro IDE and choose aidlc in the chat panel's agent picker, or run `kiro-cli`	`/aidlc`	Kiro IDE
Kiro CLI >= 2.6 (legacy)	`aidlc config --harness kiro`	`kiro-cli chat`	`/aidlc`	Kiro CLI
Codex CLI >= 0.145.0	`aidlc config --harness codex`	`codex`	`$aidlc`	Codex CLI
Cursor	`aidlc config --harness cursor`	Open Cursor or run `agent`	`/aidlc`	Cursor
opencode >= 1.17	`aidlc config --harness opencode`	`opencode`	`/aidlc`	opencode
GitHub Copilot CLI >= 1.0.74 / VS Code >= 1.130	`aidlc config --harness copilot`	Copilot CLI or VS Code	`/aidlc`	GitHub Copilot

Model-provider setup belongs to the harness. Shipped project configuration keeps the provider and model already selected by the user. aidlc config providers can apply Amazon Bedrock settings on supported project surfaces or record manual setup for other harnesses. Kiro CLI and Kiro IDE need no provider answer because model access comes with Kiro. On Kiro CLI 2.24.1 or later, use kiro-ide, which pins Kiro's v3 engine; kiro is the legacy row (Kiro CLI says how an existing kiro project switches). The methodology itself is provider-independent.

Recommended Model

AI-DLC works best with capable reasoning models. The current recommended model is Claude Opus 4.8. If Opus is not available to you, or you are not sure where to set the model and effort on your harness, see Choosing a Model and Effort.

Why AI-DLC

Ad-hoc AI coding loses context as projects grow. AI-DLC keeps requirements, decisions, implementation, tests, and operational work connected through one audited lifecycle:

5 phases and 33 stages from initialization through operation
14 agents: 11 domain experts, 2 reviewers, and an adaptive composer
11 workflow profiles for features, bug fixes, infrastructure, security, proofs of concept, enterprise delivery, and other common work
Human approval gates and source-bound review evidence
119-event audit trail plus persistent state, team knowledge, and learned rules
The same deterministic engine across every supported harness

Start with Workflow Profiles to compare Classic, Express, and the focused workflows. See the AI-DLC Workflows 2.0 Specification for the architecture and methodology.

[!IMPORTANT] Generative AI can make mistakes. Review generated output and costs before acting on them. See the AWS Responsible AI Policy.

（注：更多技术实现细节与完整 API 文档请查阅原项目 README）