AIDLC Tools & Implementation
This section covers tools and technology stacks for implementing the AIDLC methodology in real-world projects. From AI coding agents to open-weight model utilization, EKS-based declarative automation, and technology investment roadmaps, we provide practical implementation guides.
Structure
Table 1. Key content and target audience by AIDLC tool
| Document | Key Content | Target Audience |
|---|---|---|
| AI Coding Agents | Spec-Driven development on the AIDLC Vibe workflow, Q Developer, agent comparison | Developers, Tech Leads |
| Open-Weight Models | On-premises deployment, cloud vs self-hosting TCO, data residency | Architects, Security Officers |
| EKS Declarative Automation | Managed Argo CD, ACK, KRO, Gateway API | Developers, DevOps |
| Technology Roadmap | Build-vs-Wait decision matrix, investment planning | CTO, Enterprise Architects |
Tool Selection Decision Flow
Figure 1. Tool decision flow by inference-data processing boundary and coding agent selection
This branch does not ask about "data residency" as a simple yes/no; it distinguishes the inference-data processing boundary from data-storage residency. If data cannot leave the boundary (on-premises, a specific VPC, etc.) during inference, choose a self-hosted open-weight model. If only the storage location must be confined to a specific region while processing can run on managed services, you can use a managed coding agent such as Kiro or Q Developer while pinning the data-storage region.