AI-DLC Explained: How AWS Labs Brings Structure to AI Coding

Understanding the AWS AI-DLC

AWS AI-DLC (AI-driven development lifecycle) is a formal, structured workflow framework that guides AI coding assistants through a formal software development process. This framework enforces mandatory workflows using "steering files" (rules) that dictate how the AI must operate.

The structure of this workflow consists of three phases:

1. It starts with inception. That's where all your requirements are analysed, and workflow planning gets done.
2. Only then can it move into construction, which is the development, the functional design, the code generation, and testing.
3. There is a third phase, operations, where it’s deployed.

The framework requires users to provide approval at key points before moving to the next phase. You cannot go from inception to construction, or from planning to code generation, without someone signing off. This step-by-step process helps prevent the AI from making changes on its own or acting in unexpected ways.

AWS AI-DLC puts documentation first. All files go into `aidlc-docs/` subfolders based on their phase, while the application code stays in the main workspace and never in the documentation folders. This setup means that even if the code is deleted, the record of what was done is still there.

Breaking down AWS AI-DLC’s rule set

Now let's talk about the rules themselves. The repository shows them broken down by phase `inception/` (includes 7 rule files like workspace detection, requirements analysis, user stories, application design, etc.), `construction/` (includes 6 rule files such as functional design, code generation, NFR requirements, infrastructure design, build/test), and `common/` (shared validation rules like content validation, ASCII/Mermaid diagram standards).

Critical mandatory rules:

• Content validation: all files must pass validation according to `common/content-validation.md` before they are created.
• Rule details logging: load the relevant rule detail files from `.kiro/aws-aidlc-rule-details/` before starting each phase.
• Comprehensive audit logging: log every user interaction, approval, and decision in `audit.md`, including all raw input and timestamps.
• Checkpoint approvals: users must explicitly approve before advancing between major stages.
• Plan-level checkboxes: mark each work step with [x] as soon as it is completed.
• No emergent behaviour: during construction phases, use only standardised 2-option completion messages.

The AWS AI-DLC process: requirements analysis to code generation

AWS AI-DLC framework takes what could be messy AI input and turns it into a clear, repeatable workflow. The logging stands out. Every step, from requirements analysis to workflow planning and code generation, gets tracked in detail. Each decision is recorded in an `audit.md` file, making traceability a key feature.

Requirements analysis

Requirements analysis is a structured nine-step execution process flow:

1. Load context: retrieve reverse-engineering docs for brownfield projects.
2. Analyse request: evaluate clarity, type, scope, and complexity.
3. Determine depth: choose minimal, standard, or comprehensive analysis level.
4. Assess provided materials: review existing requirements and intent statements.
5. Completeness analysis: systematically evaluate six requirement categories.
6. Generate questions: create a verification document; ask until ambiguities resolve.
7. Build requirements doc: synthesise findings with user answers.
8. Update tracking: mark Inception Phase progress complete.
9. Present results: deliver a structured completion message with the approval workflow.

Once the requirements are set, the next step is planning how the code will come together. This is where workflow planning kicks in. Instead of jumping straight into coding, the AI lays out a clear, step-by-step plan for building the software.

Code generation process

Part 1 - Planning: Create a detailed, step-by-step code generation plan with explicit activities and story traceability.

Part 2 - Generation: Execute the approved plan sequentially, generating code, tests, and artifacts while updating progress checkboxes.

Critical rules:

• Application code in workspace root only; documentation in `aidlc-docs/` only
• Brownfield projects modify existing files in-place rather than creating duplicates
• "Do not deviate from the step sequence"
• Never add hardcoded logic beyond specifications

How AWS AI-DLC differs from standard AI development

AWS AI-DLC works best when your requirements are predictable, several stakeholders need to see how AI makes decisions, you’re dealing with old code that needs to be understood, your team is onboarding and needs consistent AI behaviour, or when auditability is more important than how fast you develop.

On the other hand, traditional practices are better if you need to prototype quickly and your requirements change often, you’re working alone without managing stakeholders, you’re exploring solutions that aren’t clear yet, you have tight deadlines and need to ship fast instead of documenting everything, or you need flexibility because AI-DLC ties you to Amazon Q or Kiro.

Detailed comparison of AWS AI-DLC vs alternative AI development approaches

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AWS AI-DLC limitations to consider

• IDE tool lock-in: Rules designed specifically for Amazon Q and Kiro CLI - may not work with other AI coding assistants.
• Documentation overhead: Every phase generates multiple markdown files (requirements.md, audit.md, plans, designs, etc.).
• Approval friction: Mandatory checkpoints between phases require explicit user confirmation.
• Learning curve: 20+ rule files to understand; requires knowing when to invoke "Using AI-DLC, ..." trigger phrase.
• Activation: Users initiate projects by stating intent using "Using AI-DLC, ..." in chat. The workflow then presents structured multiple-choice questions, generates plans for user review, and produces artifacts in an `aidlc-docs/` directory after each approved stage.
• Implementation: AI-DLC activates through platform-specific steering mechanisms: Amazon Q Rules files in `.amazonq/rules/` or Kiro Steering Files in `.kiro/steering/`, copied from the `aidlc-rules` repository.

Conclusions

This is a framework that forces mandatory quality gates, treats documentation as a first-class, mandatory deliverable, and gives you built-in traceable audit trails for every single decision.

The AWS AI-DLC proves that formalising these AI coding processes is less about making coding easier and is entirely about making AI coding governable. We can automate the generation of features, sure, but we can't automate the human trust that's needed to actually deploy them. And that raises a really critical question that everyone needs to consider as development just keeps accelerating.

If we accept the premise that AI needs these strict human-gated limitations to avoid emergent behaviour and maintain compliance, what is the ultimate trade-off that society is making? We're weighing the incredible speed of autonomous iteration against the safety and accountability of human-gated progression. That tension, that's probably the defining characteristic of the next decade of software development.