AI-DLC is a framework that turns AI coding assistants into structured, verifiable software-delivery workflows.
The problem AI-DLC addresses is that ad-hoc AI coding loses context as projects grow, leaving requirements, decisions, implementation, tests, and operational work disconnected. The tool solves this by providing a harness-neutral core that runs natively across multiple AI coding agents—Claude Code, Kiro CLI, Kiro IDE, Codex CLI, Cursor, opencode, and GitHub Copilot—keeping requirements, decisions, implementation, tests, and operational work connected through one audited lifecycle. Users configure a project with their chosen harness, then invoke workflows through simple commands that guide the AI agent through structured steps with approval gates before moving forward.
Developers should choose this tool if they work with AI coding assistants and want to impose structure and auditability on their workflows rather than relying on ad-hoc prompting. It suits teams using any of the supported harnesses who need their AI agents to follow consistent processes and maintain decision history. The tool works with capable reasoning models and is provider-independent, though it defaults to Amazon Bedrock for Claude Code and Codex CLI, while other harnesses use their own configured providers.
The project maintains active development with regular updates to support new harness versions and capabilities. The codebase is written in TypeScript and distributed both as a native executable and as downloadable runtime files for manual project integration. Documentation covers harness-specific setup guides and a complete getting-started walkthrough, indicating sustained attention to user onboarding across its multi-harness ecosystem.
