This is an example output from the automated design review system described in The Ultimate Design Review: Orchestrating AI with Task-Based Workflows - Part 6 of 6.

This post shows how the AI transforms the comprehensive analysis into a structured, actionable backlog. The system automatically generates specific tasks with clear implementation steps, acceptance criteria, and testing requirements, turning qualitative feedback into a quantitative project plan.

Task Status Report: Design Review 2025-09-06 Action Items

Generated on: 2025-09-07

This is an example output from the automated design review system described in The Ultimate Design Review: Orchestrating AI with Task-Based Workflows - Part 6 of 6.

This post shows the comprehensive analysis that the AI generates when performing a full-scale design review of a codebase. The AI systematically analyzes each layer of the application, identifies architectural strengths and issues, and provides detailed findings that form the foundation for actionable improvement tasks.

Part 5 of 6: The AI-Assisted Development Workflow Series

This is the fifth installment in a six-part series exploring how AI is transforming modern development workflows. In this series, I’ll walk through my journey of building an AI-assisted development environment, from basic infrastructure setup to advanced architectural enforcement and task orchestration.

Series Overview:

• Part 1: MCP Servers, Ports, and Sharing - Setting up the foundation
• Part 2: ESLint Configuration Refactoring - Cleaning up tooling with AI
• Part 3: Custom Architectural Rules - Teaching AI to enforce design patterns
• Part 4: Task Orchestration - Managing complex refactoring workflows
• Part 5: Project Rules for AI - Creating effective memory banks and guidelines
• Part 6: The Ultimate Design Review - Putting it all together

In the previous posts, we’ve taught our AI assistant how to understand and enforce specific architectural rules. We’ve built a robust system for linting, testing, and managing complex tasks. But how do we ensure the AI behaves consistently and predictably over the long term? How do we give it a “personality” and a “memory” that aligns with our project’s philosophy?

Part 6 of 6: The AI-Assisted Development Workflow Series

This is the final installment in our six-part series on building an AI-assisted development workflow. We’ve set up our infrastructure, taught the AI our coding standards, and established a robust “memory bank” of project rules. Now, it’s time to put it all to the test with the ultimate challenge: a comprehensive, end-to-end design review of the entire codebase.

Series Overview:

• Part 1: MCP Servers, Ports, and Sharing - Setting up the foundation
• Part 2: ESLint Configuration Refactoring - Cleaning up tooling with AI
• Part 3: Custom Architectural Rules - Teaching AI to enforce design patterns
• Part 4: Task Orchestration - Managing complex refactoring workflows
• Part 5: Project Rules for AI - Creating effective memory banks and guidelines
• Part 6: The Ultimate Design Review - Putting it all together

The Challenge: From “Codebase” to “Action Plan”

Want to see what we’re building? I’ve created two example posts that show the actual output from this automated design review system: