Aidlc: Adaptive workflow steering rules for AI coding agents

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: curl -fsSL https://github.com/awslabs/aidlc-workflows/releases/latest/download/install.sh | sh Windows 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/ / 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: 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: /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 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. For Kiro's v3 engine on Kiro CLI 2.24.1 or later, use kiro-ide, which pins it; kiro is Kiro CLI (V2) (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. Documentation Repository Layout core/ - hand-authored, harness-neutral methodology and engine core/tools/ - 93 aidlc-*.ts engine and authoring tools harness/ / - thin, harness-specific manifests and integrations plugins/ / - optional AIDLC plugins scripts/ - packaging, binary, installer, and release tooling tests/ - smoke, unit, integration, and end-to-end tests docs/ - user, harness-engineering, and developer documentation dist/ and dist-release/ - generated, ignored local outputs Edit core/ or harness/ /, never generated dist* output. Development Install dependencies and generate every harness: bun install --frozen-lockfile bun scripts/package.ts Useful commands: bun scripts/package.ts # generate one harness bun scripts/package.ts --check # determinism guard bun tests/run-tests.ts --ci # smoke, unit, and integration bun tests/run-tests.ts --release # full release acceptance See the Contributing Guide for the complete development workflow and Porting to a New Harness to add another runtime. Troubleshooting Run aidlc doctor from the project root first. Common fixes: See Troubleshooting for diagnostic and recovery procedures. References AWS AI-DLC blog post AI-DLC Method Definition Paper Roadmap License