AIDLC Methodology
This section builds on AWS Labs AIDLC Workflows (v0.1.7, 2026-04-02), adding DDD, Ontology, and Harness extensions. It follows the official 5 principles, 11 Common Rules, and 7-stage Adaptive Execution as published, while engineering-playbook adds the ontology and harness axes as its own extension for enterprise reliability.
This track explains how AIDLC organizes collaboration between people and AI during development. It introduces the Intent → Unit → Bolt execution model, then explains how domain schemas, validation controls, and domain-driven design support that workflow.
Structure
The methodology track consists of 6 documents (4 core documents plus 2 official-alignment documents). Reading them in order will help you understand the entire theoretical framework of AIDLC. The figure below shows the reading order of the 4 core documents.
Figure 1. Reading order and relationships of the four core methodology-track documents
Table 1. Methodology-track document structure and the core question of each document
| Order | Document | Core Question |
|---|---|---|
| 1 | 10 Principles and Execution Model | What is AIDLC and how does it work? (official 5 principles + Intent/Unit/Bolt mapping) |
| 2 | Ontology Engineering (extension) | How do we ensure the accuracy of AI-generated code? (extension) |
| 3 | Harness Engineering (extension) | How do we architecturally enforce the safety of AI execution? (extension) |
| 4 | DDD Integration | How do we transform business domains into designs that AI can understand? |
| 5 | Common Rules (official) | What are the 11 official AIDLC Common Rules and how are they applied? |
| 6 | Adaptive Execution (official) | When and how do the official Inception 7-stage flow and the Construction per-Unit loop run? |
(official) AWS Labs official AIDLC alignment document
(extension) engineering-playbook's own extension (enterprise reliability)
Relationship with Other Tracks
- Enterprise Adoption: Interprets the methodology's concepts (ontology, harness) as organizational transformation and cost effectiveness.
- Tools & Implementation: Covers concrete tools (Kiro, Q Developer, EKS) that realize the methodology.
- AgenticOps: Builds a circular structure where operational data feeds back into the ontology Outer Loop.