End-to-End Example Project

This section brings everything together. We will walk through the creation of a real-world product: “AutoSupport”, an AI-powered customer support ticketing system.

The Scenario

Client: “Need a system where users email support@company.com, and an AI automatically answers common questions or routes complex ones to a human.”

1. Planning: The Generative Backlog

Input: Meeting recording with the Client. AI Role:

• Transcribes the call.
• Generates the PRD: “System must classify emails as ‘Billing’, ‘Tech’, or ‘General’.”
• Generates User Stories:
  • “As a User, I want instant replies to password reset requests.”
  • “As an Agent, I want to see a summary of the email thread.”

2. Design: Architecture & Schema

Input: The PRD. AI Role:

• Architecture: Suggests an Event-Driven architecture (Email -> SendGrid -> Azure Function -> OpenAI -> CRM).
• Schema: Generates the SQL schema for Tickets and Messages tables.
• Mermaid Diagram:

graph LR
    Email[Inbound Email] -->|Webhook| Func[Azure Function]
    Func -->|Classify| LLM[Azure OpenAI]
    LLM -->|Billing?| QueueB[Billing Queue]
    LLM -->|Tech?| QueueT[Tech Queue]
    QueueB -->|Auto-Reply| Reply[SendGrid]

3. Build: Multi-Agent Coding

Input: The Architecture + Schema. AI Role:

• Agent A (Database): Writes the Entity Framework Core models and migration scripts.
• Agent B (Logic): Writes the Azure Function code to parse the webhook and call OpenAI API.
• Agent C (Tests): Writes Unit Tests for the “Classification Logic” (e.g., ensuring “my bill is wrong” goes to Billing).

Human Role: Reviews the Classification Prompt to ensure it’s not rude.

4. Test: Self-Healing QA

Input: The deployed Staging environment. AI Role:

• Generates synthetic emails: “Hey, I forgot my password”, “Why is my bill $500?”
• Runs integration tests: Sends email -> Checks database -> Verifies correct Queue.
• Self-Healing: A UI test fails because the “Ticket ID” field moved. AI fixes the selector.

5. Deploy: Risk-Gated Release

Input: Pull Request to main. AI Role:

• Risk Analysis: “Changes affect the Email Processing Logic. Risk Score: 78/100.”
• Action: Triggers “Manual Approval” gate.
• IaC: Updates the Terraform to add a new Queue.

6. Operate: AIOps

Input: Production Logs. AI Role:

• Alert: “OpenAI API Rate Limit Exceeded.”
• RCA: “Agent retries are too aggressive.”
• Fix: “Suggest implementing Exponential Backoff.”

Full Workflow Diagram

sequenceDiagram
    participant PM as Product Manager
    participant Arch as Cloud Architect
    participant Dev as Developer
    participant QA as QA Engineer
    participant Ops as DevOps
    
    rect #C8E6FF
    Note over PM: 1. Plan
    PM->>AI: Generate Backlog
    end
    
    rect #FFFFC8
    Note over Arch: 2. Design
    Arch->>AI: Generate Architecture
    end
    
    rect #C8FFC8
    Note over Dev: 3. Build
    Dev->>AI: Scaffold Code
    end
    
    rect #FFE6C8
    Note over QA: 4. Test
    QA->>AI: Generate Test Data
    end
    
    rect #E6C8FF
    Note over Ops: 5. Deploy & Run
    Ops->>AI: Analyze Risk & Logs
    end

Conclusion

By applying the AI-Driven SDLC to AutoSupport, the team:

• Saved 40% of time on Requirement Gathering.
• Saved 60% of time on Boilerplate Code.
• Caught 3 critical bugs via AI-generated test cases.
• Deployed with confidence due to AI risk scoring.