🚨 The Problem: How Do Architects Enable Speed Without Sacrificing Safety?
In today’s enterprise environments, architecture teams are under pressure to deliver faster — but without compromising on security, scalability, or operational resilience. The tension is real:
- Delivery velocity demands rapid experimentation, short feedback loops, and lean processes.
- Governance demands compliance, traceability, and architectural integrity.
Traditional architecture practices — heavy modeling, gated reviews, and rigid documentation — often slow down innovation. Meanwhile, agile teams want autonomy and speed. The result? Either governance is bypassed, or delivery is delayed.
Enter vibe coding — a generative AI-powered approach that lets architects and developers co-create solutions rapidly, while still embedding guardrails and governance into the software development lifecycle (SDLC).
🎯 What Is Vibe Coding?
Vibe coding is a lightweight, AI-assisted approach to software ideation and prototyping. You chat with an AI assistant, sketch code, explore infrastructure options, and validate ideas — all in minutes.
For developers, it’s liberating. For architects, it’s a superpower — if used wisely.
This post shows how architects can use vibe coding to:
- Frame problems clearly.
- Prototype fast.
- Codify guardrails.
- Empower teams.
- Maintain governance across the SDLC.
🧩 Step 1: Inception — Frame the Problem, Lightly
Start with clarity, but keep it lean.
Scenario: “We need a Notification Orchestration Service that sends alerts to customers across email, SMS, and push channels — triggered by business events like payment failures or fraud detection.”
- Business Capability: Multi-channel notification orchestration.
- Value Stream: Event-driven customer communication.
- Non-Negotiables:
- Security: Microsoft Authentication Library (MSAL) integration.
- Latency: Less than 200 milliseconds for push notifications.
- Observability: Azure Application Insights with correlation IDs.
- Cost: Less than $0.01 per notification.
🧱 What Is an Architecture Decision Record (ADR)?
An Architecture Decision Record (ADR) is a lightweight document that captures a key technical decision made during the design of a system. It helps teams understand the rationale behind choices — especially when revisiting them months or years later.
A skeleton ADR is a simple, early-stage version of this document. It’s not meant to be exhaustive — just enough to set direction and spark alignment.
Basic structure:
- Problem Statement: What are we solving?
- Constraints: What must be respected?
- Success Criteria: What does “done” look like?
🤖 Step 2: Vibe Ideation — Explore With AI
Treat your AI assistant like a pair-programming partner. Ask it to generate scaffolding:
- “Create a .NET 8 Durable Function orchestrator with retry and App Insights logging.”
- “Generate a Helm chart & Azure DevOps pipeline for Elastic Kubernetes Service (EKS).”
- “Compare Azure App Service vs Azure Functions vs Azure Kubernetes Service (AKS) for cost and reliability.”
Explore what-ifs:
- “What’s the latency difference between Azure Notification Hubs and Twilio?”
- “How would retries work with exponential backoff?”
⚡ Step 3: Spike & Proof of Concept — Fast Reality Check
Take the AI-generated output and spin up a thin spike — a minimal implementation that tests feasibility.
- Minimal domain entities (e.g., NotificationEvent, ChannelConfig).
- Logging and telemetry baked in.
- Quick CI/CD to a sandbox.
Run checks:
- Latency under load.
- Cost per notification.
- Deployment friction.
🛠️ Step 4: Architect Review & Hardening
Convert proof-of-concept learnings into hardened reference implementations:
- Clean repository structure (e.g., domain-driven design, queue-triggered durable functions).
- Updated Architecture Decision Records (ADRs) with rationale.
- Architecture diagrams: context, deployment, flow.
- Security and operations reviews.
🧱 Step 5: Codify Guardrails
🧠 Gold-Standard Prompt for Generative AI
You are a Principal Enterprise Architect leading the digital transformation for a Fortune 500 company. Your task is to generate the gold-standard boilerplate for a secure, highly resilient, event-driven .NET 8 Minimal API that will serve as the foundation for a global-scale Notification Orchestration Service.
The response must not just list components, but provide demonstrable, production-ready code snippets and justifications for the architectural decisions.
Core Requirements (Elevated)
- Authentication & Authorization (Zero Trust):
- Microsoft Identity Platform (Azure Active Directory): Secure the API using Microsoft Identity Web for robust token validation (JSON Web Token Bearer).
- Role-Based Access Control (RBAC): Implement a sample policy for authorization that checks for a specific application role (e.g., Notification.Admin).
- Managed Identities: Define how the API will use Azure Managed Identity for secure, password-less access to Azure Key Vault and other Azure resources.
- Data Access (Modern & Performant):
- Entity Framework Core (EF Core) 8: Use modern EF Core features like compiled queries and raw SQL execution for critical paths.
- Sample Model: Include NotificationEvent entity with an Optimistic Concurrency token (e.g., a RowVersion or timestamp).
- Transactional Outbox Pattern: Integrate a basic mechanism (e.g., a dedicated table/repository) for supporting the transactional outbox pattern to ensure atomicity between database writes and event publishing.
- Protocol & Querying:
- Open Data Protocol (OData) v4: Integrate OData for resource querying.
- API Design: Utilize Minimal APIs for routing and handling, but encapsulate logic within Endpoint Handlers (Mediator pattern via MediatR or Carter).
- Resilience & Observability (Site Reliability Engineering Focus):
- OpenTelemetry: Use OpenTelemetry for logging, metrics, and tracing, integrating with Azure Application Insights.
- Correlation & Structured Logging: Implement middleware to ensure a consistent correlation ID across all logs and traces. Use Serilog for structured logging.
- Health Checks: Include Liveness and Readiness endpoints using Microsoft.Extensions.Diagnostics.HealthChecks.
- Dependency Injection (DI):
- Utilize the built-in .NET DI container.
- Demonstrate Scrutor usage for automatic service registration of interfaces and implementations within the Clean Architecture layers.
- Performance & Asynchrony:
- Asynchronous Patterns: Ensure all I/O bound operations use async/await correctly, including IAsyncEnumerable for streaming results.
- Performance Constraints: All service logic must be cancellation-token aware.
- Project Structure (Enterprise Clean Architecture):
- Strict adherence to Clean Architecture: Domain, Application (Commands/Queries and Handlers), Infrastructure (Persistence, External Services), and API (Presentation/Endpoints).
- Use Project References to enforce dependency rules (e.g., Domain cannot reference Infrastructure).
- DevOps & Infrastructure-as-Code (IaC):
- GitHub Actions CI/CD: Provide a multi-stage sample workflow (Build, Test, Publish, Deploy).
- IaC (Bicep or Terraform): Provide a snippet for deploying the API to Azure Container Apps or Azure App Service, configuring a Virtual Network and Azure SQL Database.
- Documentation & Governance:
- README.md: Detailed setup guide, explaining the use of Managed Identity and Key Vault for configuration.
- Architecture Decision Record (ADR): Justify the choice of Minimal APIs with Carter/MediatR over traditional Controllers.
- Extras & Advanced Patterns:
- Unit & Integration Tests: Provide a sample unit test using a mocking framework and an integration test using WebApplicationFactory and an in-memory database (SQLite).
- API Contracts/Versioning: Define a sample Request/Response Data Transfer Object (DTO) and show how API versioning would be managed.
Required Output Format:
- GitHub-Style Repository Layout (Zipped folder structure is too cumbersome).
- Key Code Snippets for the most complex/opinionated components (e.g., Program.cs for setup, CorrelationMiddleware, an Endpoint Handler, and the HealthCheck setup).
- Concise Commentary explaining the purpose and enterprise-grade nature of each architectural choice (e.g., Why OpenTelemetry? Why Transactional Outbox?).
🚚 Step 6: Delivery — Empower Teams
Give teams a ready-to-go starter pack:
- Repositories with orchestration samples.
- Pipelines wired for tests and security scans.
- Clear prompts and ADRs.
Let developers vibe-code new features inside a safe, governed frame.
🔁 Step 7: Continuous Feedback & Evolution
After first releases:
- Review Azure Application Insights, cost dashboards, and Azure Advisor recommendations.
- Update ADRs and templates with new lessons.
- Host architecture office hours to guide teams and answer questions.
🧭 The Takeaway
Vibe early, govern late.
Architects don’t have to choose between speed and safety. With generative AI, you can prototype fast, validate early, and lock down standards as you move toward delivery.
This is the future of enterprise architecture: Lean, agile, AI-augmented, and business-aligned.
⚠️ Responsible Use of AI
While generative AI can accelerate development and ideation, it is essential to:
- Always review and vet AI-generated code for security, performance, and compliance.
- Ensure sensitive data is not exposed in prompts or outputs.
- Validate architectural decisions with human oversight and domain expertise.
- Use AI as a collaborative assistant, not a replacement for critical thinking and governance.
Responsible AI usage ensures that innovation remains safe, ethical, and aligned with enterprise standards.
