Stage 2: AI Integration

Timeline: Next 3-6 Months

This is the most critical transformation phase. We move beyond “using AI tools” to “integrating AI into the workflow.” This stage fundamentally changes how we build software, shifting from Code-First to Spec-First.

Why This Stage Matters

Individual productivity (Stage 1) is good, but systemic velocity is better. By integrating AI into the entire SDLC—from requirements usage to automated testing—we unlock exponential gains in delivery speed and quality.

Core Concept

Spec-Driven Development: We write detailed, prompt-ready specifications (Markdown/Gherkin) that AI agents use to generate code, tests, and documentation.

The AI-Driven SDLC

graph LR
    A[Spec Definition] -->|AI Analysis| B[Code Generation]
    B -->|AI Review| C[Automated Testing]
    C -->|AI Analysis| D[CI/CD Pipeline]
    D -->|Deploy| E[Production]
    
    subgraph "The Feedback Loop"
    C -.->|Failure Analysis| A
    end
    
    style A fill:#d4f1f9,stroke:#0077b6
    style C fill:#e2f0cb,stroke:#55a630

Key Activities & Deliverables

Activity	Description	Deliverable
Spec-Driven Dev	Adopting Markdown-based PRDs and Tech Specs.	Standardized Templates for AI-ready specs.
AI Testing	Automated test generation and self-healing tests.	Playwright + AI integration framework.
AI CI/CD	AI agents reviewing PRs and analyzing build failures.	GitHub Actions workflows with AI reviewers.
LLMOps Basics	Setting up observability for AI components.	Langfuse deployment for tracing.

Tools & Platforms

• Spec & Design: Markdown, Mermaid, Gherkin.
• Orchestration: LangChain, Semantic Kernel.
• Testing: Playwright, Allure.
• CI/CD: GitHub Actions, Azure DevOps.

Engineering Workflow Transformation

Traditional Workflow

1. Read vague Jira ticket.
2. Write code manually.
3. Write tests manually.
4. Manual code review.
5. Fix bugs found in QA.

Stage 2 AI Workflow

1. Refine Spec with AI until crystal clear.
2. Generate Code using Spec + Copilot/Cursor.
3. Generate Tests from Spec automatically.
4. AI Pre-Review checks style and logic.
5. Human Review focuses on architecture.

Success Metrics

• Development Velocity: 2-3x faster feature delivery.
• Defect Density: >40% reduction in bugs reaching QA.
• Spec Quality: 100% of new features have an “AI-Ready” spec.

Real Example: “The 2-Day Sprint”

Scenario: Building a new CRUD API service with authentication.

Goal: Complete implementation, testing, and docs.

Stage 2 Approach:

1. Engineer writes a strict OpenAPI spec and a prompt describing business logic.
2. AI scaffolds the Project (Controller, Service, Repository).
3. AI generates Unit and Integration tests based on the spec.
4. Engineer reviews, refines, and merges.

Result: What took 1 week is now done in 2 days.