AI-DLC Workflows: AI-Driven Life Cycle Steering Rules
/aidlc-workflows
AI-DLC Workflows works as advertised for teams running autonomous AI agents on well-scoped tasks — the phase-gated structure genuinely reduces the kind of off-rails code generation that wastes a sprint. But it demands manual setup per project and a developer willing to review AI output carefully. Reach for it when you need an agent to follow a repeatable SDLC rather than just generating code; skip it if your team treats AI as a chat-with-clipboard and the overhead of maintaining workflow rules isn't worth the payoff.
What are AI-DLC Workflows?
aidlc-workflows is a curated set of steering rules that govern how an AI coding agent behaves across the software development lifecycle, dividing development into named phases like inception, construction, extensions, operations, and common. These conditional rule files activate based on the agent's current context. They reside as markdown files in the project directory and are loaded by each supported agent platform's native rules mechanism. Version 2.0 introduced stricter phase-gating.
The Challenge of Autonomous Software Development
AI coding agents default to producing code without stopping to clarify requirements, verify correctness, or consider operational impact. Teams adopting autonomous agents at scale find that agents diverge from project conventions, skip edge cases, and generate code that passes a test suite but doesn't survive a deployment. The gap is behavioral guidance — not code generation capability, but workflow discipline that keeps AI agents operating as engineering teammates rather than unconstrained code printers.
Core Capabilities of AI-DLC Workflows
- ✓Phase-gated development with explicit transitions between inception, construction, extensions, and operations stages
- ✓Conditional rule loading — the agent activates relevant detail files based on current workflow phase
- ✓Multi-agent support covering Kiro, Amazon Q Developer, Cursor, Cline, Claude Code, GitHub Copilot, and OpenAI Codex
- ✓Version 2.0 specification published as a PDF, making the workflow rules auditable and forkable
- ✓Cross-platform setup scripts for macOS, Linux, and Windows across all supported agents
- ✓Project-scoped rule storage — each project carries its own AI-DLC configuration in dotfiles
- ✓Separation of core workflow rules from detailed sub-rules, allowing teams to trim or extend behavior
- ✓MIT-0 license with no commercial restrictions on adoption or modification
Setting Up AI-DLC Workflows
Download the latest release zip named `ai-dlc-rules-v<release-number>.zip` from the Releases page on GitHub. Extract it to a folder outside your project directory — the README specifically recommends `~/Downloads`. The extracted contents include two subdirectories: `aws-aidlc-rules/` (the core workflow file) and `aws-aidlc-rule-details/` (phase-specific conditional rules). Copy both into your project using the platform-specific paths documented for your agent. For example, with Claude Code on macOS: copy `core-workflow.md` to `CLAUDE.md` in the project root and copy the `aws-aidlc-rule-details/` folder into `.aidlc-rule-details/`. Verify the setup by running the agent's config command and confirming the rules appear in the active instruction set.
Integrating AI-DLC with Your Coding Agent
After installation, AI-DLC Workflows operates as a passive constraint layer — you interact with your coding agent as normal, but the steering rules govern how the agent responds. When the agent enters a new phase (for example, moving from inception to construction), it loads the corresponding detail files and adjusts its behavior accordingly. The README recommends staying in Vibe mode when using Kiro to allow the AI-DLC workflow to guide development without interruption. For Cursor, you can use project rules via `.cursor/rules/ai-dlc-workflow.mdc` with `alwaysApply: true`, or fall back to `AGENTS.md`. Each agent platform surfaces the rules differently — Kiro shows them in the IDE steering panel, Amazon Q exposes a Rules button in the chat window, and GitHub Copilot reads `.github/copilot-instructions.md` automatically.
Strengths
- ✓Multi-agent compatibility means you can adopt it without switching coding tools — it runs on the agent you already use
- ✓The MIT-0 license removes IP concerns for commercial projects — no attribution required, no restrictions
- ✓The phase-gated structure provides real behavioral scaffolding, not just style preferences
- ✓Project-scoped configuration means teams can customize rules per repository without affecting others
- ✓Cross-platform support with separate instructions for macOS, Linux, Windows PowerShell, and CMD
- ✓Version 2.0 specification is publicly documented as a PDF, enabling community auditing and forks
Considerations for Using AI-DLC
- △Setup requires manual file copying per project — there is no CLI installer or package manager that automates this
- △Rule maintenance is entirely DIY — if AWS Labs updates the rules, you must re-download and re-copy the files manually
- △The phase model assumes a certain development style that may conflict with highly iterative or CI-driven workflows
- △Agents can still ignore or misinterpret rules — the README explicitly warns to review all AI-generated output
- △No built-in analytics or reporting on whether agents are actually following the workflow phases
Exploring AI Agent Workflow Options
Frequently Asked Questions
The AI-DLC Methodology is a structured approach to software development that breaks the lifecycle into named phases — inception, construction, extensions, and operations. AI-DLC Workflows encodes these phases as machine-readable rules that steer an AI coding agent's behavior at each stage, forcing deliberate transitions rather than letting the agent free-generate continuously.
AI-DLC Workflows integrates with seven agents as of version 2.0: Kiro, Amazon Q Developer, Cursor, Cline, Claude Code, GitHub Copilot, and OpenAI Codex. Each uses its native rules mechanism — Steering Files, Cursor Rules, CLAUDE.md, AGENTS.md, or Copilot custom instructions — but the underlying rule content is identical across platforms.
The README includes an explicit warning that generative AI can make mistakes and that all output should be reviewed before use. AI-DLC Workflows adds behavioral structure but does not guarantee correctness — a phase-gated agent can still produce buggy code, miss edge cases, or generate costly API calls. The workflow is a governance layer, not a verification engine.
AI-DLC Workflows is licensed under MIT-0, which is the Creative Commons zero-public-dedication variant. This means no attribution is required and there are no commercial use restrictions — teams can adopt, fork, and embed the rules in proprietary projects without legal friction.
Download the latest release zip from the Releases page on the GitHub repository — the file is named `ai-dlc-rules-v<release-number>.zip`. The README specifies extracting it to a location outside your project directory (such as `~/Downloads`) before copying the rule files into individual project workspaces.
Best use cases
- •Guiding a junior developer or non-technical team member using an AI agent through a structured, reviewable workflow
- •Onboarding a new project where you want AI agents to respect existing conventions and phase gates
- •Running autonomous coding agents on feature work that requires spec clarification before implementation
- •Establishing a consistent engineering baseline when multiple team members use different AI coding tools
- •Evaluating AI agent behavior in a sandboxed environment with measurable workflow transitions
- •Building internal tooling that extends AI-DLC rules with team-specific quality gates
Who should try it — and who should skip
AI-DLC Workflows is worth trying if you run AI coding agents on production-bound code and want a structured process without building it from scratch. Teams standardizing on one agent platform will benefit most since the rules are set once per project. You should skip it if your workflow is already highly opinionated and automated via CI/CD pipelines, or if your developers resist the friction of phase gates when working in an agent-first manner. It is also not useful for one-off code generation tasks where a self-correcting workflow is overkill.
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