AI-DLC: Giving Structure to AI-Assisted Development
AI coding assistants are great at writing code and terrible at knowing when to write it. Ask one to build a feature and it will happily jump straight to implementation, skipping the questions a good engineer asks first: what exactly are we building, why, what are the risks, and how should it be broken down? The result is fast output that often solves the wrong problem. AWS's AI-DLC (AI-Driven Development Life Cycle) is an attempt to fix that gap. It's an open-source set of workflow rules — released by awslabs — that steer AI coding agents through a disciplined software development process instead of letting them freewheel. Importantly, it isn't a tool you install or a service you pay for. It's a methodology delivered as a bundle of markdown rules that your existing coding agent reads and follows. The core idea: methodology over tooling One of AI-DLC's stated tenets is "methodology first." The whole thing ships as plain markdown rule files that you drop into whatever your agent already uses for project instructions — CLAUDE.md for Claude Code, .cursor/rules/ for Cursor, .github/copilot-instructions.md for GitHub Copilot, .amazonq/rules/ for Amazon Q, steering files for Kiro, and so on. There's nothing to run. The agent loads the rules and its behavior changes. This makes AI-DLC deliberately agnostic . It doesn't tie you to a specific IDE, model, or vendor — any coding agent that supports project-level rules can use it. The philosophy is that a good development methodology should outlive any particular tool. A three-phase adaptive workflow At its heart, AI-DLC organizes work into three phases that mirror how thoughtful software actually gets built. The Inception phase answers what to build and why . This is where the agent does requirements analysis, creates user stories when they're warranted, sketches the application design, breaks work into units that can be built in parallel, and assesses risk and complexity before a line of code is written. The Construction phase