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Live Demo: Agent Coding – MEP QA

This advanced demonstration showcases Cursor's Agent Mode for autonomous AI development, building a comprehensive MEP Insulation Quality Assurance tool with Excel integration. Watch AI work independently for extended periods while maintaining code quality and following best practices.

🎯 Demo Overview​

Project Goal: Build a complete MEP Insulation QA tool that:

  • Reads insulation specifications from Excel registers
  • Analyzes MEP systems in Revit for insulation compliance
  • Generates detailed QA reports with non-compliance issues
  • Provides automated fix suggestions and batch updates

Duration: 90 minutes (including Q&A) AI Autonomy: 60-70% autonomous development Complexity Level: Medium

🤖 Agent Mode Capabilities​

What Agent Mode Can Do​

  • Multi-file Development: Create and coordinate multiple files simultaneously
  • Complex Business Logic: Implement sophisticated algorithms and workflows
  • Integration Patterns: Connect multiple systems and data sources
  • Error Handling: Implement comprehensive error handling and validation
  • Testing: Generate unit tests and integration tests
  • Documentation: Create comprehensive code documentation

What We'll Demonstrate​

  1. Autonomous Planning: Agent breaks down requirements into implementation plan
  2. Multi-file Architecture: Agent creates coordinated file structure
  3. External Integration: Excel reading, Revit API, report generation
  4. Quality Assurance: Code review, testing, error handling
  5. Iterative Refinement: Agent improves code based on feedback

📋 Project Requirements​

Functional Requirements​

  1. Excel Integration

    • Read insulation specifications from standardized Excel format
    • Support multiple sheet types (ducts, pipes, equipment)
    • Validate data format and completeness

  2. Revit Analysis

    • Scan all MEP systems for insulation properties
    • Compare actual vs. specified insulation
    • Identify missing, incorrect, or damaged insulation

  3. QA Reporting

    • Generate detailed compliance reports
    • Export findings to Excel with formatting
    • Include visual markup in Revit model
    • Provide fix recommendations

  4. Batch Operations

    • Support bulk insulation updates
    • Validate changes before applying
    • Rollback capability for failed operations

Technical Requirements​

  • Framework: pyRevit with WPF interface
  • Data Sources: Excel (.xlsx), Revit parameters
  • Output: Excel reports
  • Performance: Handle 10,000+ MEP elements efficiently
  • Error Handling: Comprehensive validation and user feedback

🎬 Live Agent Coding Session​

Phase 1: Project Planning (5 minutes)​

Agent Prompt:

Create a comprehensive MEP Insulation QA tool for pyRevit. The tool should read insulation specifications from Excel, analyze MEP systems in Revit for compliance, and generate detailed QA reports.

Requirements:
1. Excel integration for specification input
2. MEP system analysis for insulation compliance  
3. Detailed reporting with Excel export
4. WPF interface with progress tracking
5. Batch update capabilities

Please create a complete project structure and implementation plan.

Expected Agent Response:

  • Project architecture breakdown
  • File structure with clear responsibilities
  • Implementation sequence and dependencies
  • Technology choices and justifications

Phase 2: Core Architecture (15 minutes)​

Agent Creates Project Structure:

MEPInsulationQA.pushbutton/
├── script.py                 # Entry point
├── __init__.py               # Tool metadata
├── models/
│   ├── insulation_spec.py    # Specification data model
│   ├── mep_element.py        # MEP element wrapper
│   └── qa_result.py          # QA analysis result
├── services/
│   ├── excel_service.py      # Excel reading/writing
│   ├── revit_service.py      # Revit API operations
│   ├── qa_service.py         # Quality analysis logic
│   └── report_service.py     # Report generation
├── viewmodels/
│   ├── main_viewmodel.py     # Main window ViewModel
│   └── progress_viewmodel.py # Progress tracking
├── views/
│   ├── main_window.xaml      # Main interface
│   └── progress_window.xaml  # Progress dialog
└── utils/
    ├── validation.py         # Data validation
    └── constants.py          # Application constants

Watch Agent Work:

  • Simultaneous file creation
  • Consistent naming conventions
  • Proper dependency management
  • Architecture documentation

Phase 3: Excel Integration Service (15 minutes)​

Agent Builds Excel Service:

# services/excel_service.py - Agent autonomous development
class ExcelService:
    """Service for reading insulation specifications from Excel."""
  
    def __init__(self):
        self.workbook = None
        self.specifications = {}
  
    def load_specifications(self, file_path: str) -> Dict[str, InsulationSpec]:
        """Load insulation specifications from Excel file."""
        # Agent implements:
        # - File validation
        # - Multi-sheet processing
        # - Data parsing and validation
        # - Error handling
        # - Progress reporting

Key Agent Behaviors:

  • Understands Excel format requirements
  • Implements proper error handling
  • Creates comprehensive validation
  • Follows established patterns

Phase 4: MEP Analysis Engine (20 minutes)​

Agent Develops Analysis Logic:

# services/qa_service.py - Agent autonomous implementation
class QAService:
    """Quality assurance analysis for MEP insulation."""
  
    def analyze_mep_insulation(self, specifications: Dict) -> List[QAResult]:
        """Analyze MEP systems against insulation specifications."""
        # Agent implements:
        # - MEP element collection and filtering
        # - Insulation parameter extraction
        # - Specification comparison logic
        # - Compliance scoring
        # - Issue categorization

Advanced Agent Capabilities:

  • Complex business logic implementation
  • Performance optimization for large datasets
  • Comprehensive test case generation
  • Documentation with examples

Phase 5: WPF Interface Development (15 minutes)​

Agent Creates MVVM Interface:

<!-- views/main_window.xaml - Agent UI generation -->
<Window x:Class="MEPInsulationQA.MainWindow">
    <!-- Agent creates:
         - Professional layout
         - Data binding patterns
         - Progress indicators
         - Error handling
         - Responsive design -->
</Window>

UI Features Agent Implements:

  • File selection and validation
  • Real-time progress tracking
  • Results visualization
  • Export options
  • Error messaging

Phase 6: Report Generation (10 minutes)​

Agent Builds Reporting System:

# services/report_service.py - Agent autonomous development
class ReportService:
    """Generate comprehensive QA reports."""
  
    def generate_excel_report(self, qa_results: List[QAResult]) -> str:
        """Generate detailed Excel report with formatting."""
        # Agent implements:
        # - Multi-sheet report structure
        # - Conditional formatting
        # - Charts and summaries
        # - Export optimization

Phase 7: Integration and Testing (10 minutes)​

Agent Integrates All Components:

  • Connects services through dependency injection
  • Implements comprehensive error handling
  • Creates integration tests
  • Optimizes performance bottlenecks

💡 Effective Prompt Engineering for Agent Mode​

Initial Project Prompt Structure​

CONTEXT: Building [specific tool type] for [domain/industry]

REQUIREMENTS:
1. [Functional requirement 1]
2. [Functional requirement 2]
3. [Technical requirement 1]

CONSTRAINTS:
- Framework: [specific framework]
- Performance: [specific metrics]
- Integration: [external systems]

OUTPUT: Complete implementation with [specific deliverables]

Iterative Refinement Prompts​

REVIEW: The current implementation has [specific issue]
IMPROVE: [Specific improvement request]
MAINTAIN: [What should stay the same]

Quality Gate Prompts​

VALIDATE: Check the code for [specific quality criteria]
TEST: Create tests for [specific scenarios]
DOCUMENT: Add documentation for [specific components]

🎯 Key Demonstration Points​

Agent Autonomy Showcase​

1. Planning and Architecture​

"Notice how the Agent broke down our complex requirements into a logical project structure. It understood the need for separation of concerns and created appropriate service layers."

2. Pattern Recognition​

"The Agent recognized this was a pyRevit project and automatically applied appropriate conventions, including proper error handling patterns and pyRevit-specific imports."

3. Integration Intelligence​

"Watch how the Agent handles the complexity of integrating Excel reading, Revit API operations, and WPF UI - it understands the data flow and creates appropriate abstractions."

4. Quality Assurance​

"The Agent is proactively adding error handling, input validation, and progress reporting - quality practices we didn't explicitly request but are essential for production code."

Code Quality Observations​

Consistent Patterns​

  • Error handling follows established patterns
  • Naming conventions remain consistent
  • Documentation style is uniform
  • Architecture principles are maintained

Performance Awareness​

  • Efficient Revit API usage patterns
  • Batch processing for large datasets
  • Memory management considerations
  • Progress reporting for long operations

User Experience Focus​

  • Intuitive interface design
  • Clear error messages
  • Progress feedback
  • Graceful error recovery

🛠️ Troubleshooting Agent Development​

Common Agent Issues​

1. Context Loss​

ISSUE: Agent forgets earlier decisions
SOLUTION: Provide context reminders in prompts
EXAMPLE: "Continuing with the MVVM pattern we established..."

2. Over-Engineering​

ISSUE: Agent creates unnecessarily complex solutions
SOLUTION: Specify simplicity constraints
EXAMPLE: "Keep the solution simple and focused on core requirements"

3. Incomplete Integration​

ISSUE: Components don't integrate properly
SOLUTION: Request integration validation
EXAMPLE: "Ensure all services integrate properly with the main workflow"

Agent Guidance Techniques​

Progressive Disclosure​

1. Start with high-level architecture
2. Drill down into specific components
3. Refine implementation details
4. Integrate and test

Quality Checkpoints​

- After each major component: "Review for quality and consistency"
- Before integration: "Validate all interfaces and dependencies"
- After completion: "Perform comprehensive testing and optimization"

📊 Performance Metrics​

Development Speed​

  • Traditional Development: 2-3 weeks for comparable tool
  • Agent-Assisted Development: 2-3 days for core functionality
  • Speed Improvement: 5-7x faster development

Code Quality​

  • Consistency: 95% pattern adherence across all files
  • Documentation: 100% of public methods documented
  • Error Handling: Comprehensive coverage throughout
  • Testing: Automated test generation for core components

Learning Curve​

  • Team Adoption: 1-2 days to become productive with Agent Mode
  • Quality Assurance: Code review process adapted for AI-generated code
  • Best Practices: Established prompt engineering patterns

🎓 Audience Interaction Points​

Real-time Polls​

Question 1: Agent Capability Assessment​

"Based on what you've seen, what percentage of this project could the Agent complete autonomously?"

  • A) 30-40%
  • B) 50-60%
  • C) 70-80%
  • D) 90%+

Question 2: Biggest Surprise​

"What surprised you most about Agent Mode capabilities?"

  • A) Code quality consistency
  • B) Complex integration handling
  • C) Autonomous problem-solving
  • D) Speed of development

Interactive Challenges​

Challenge 1: Feature Addition​

"Let's add real-time Revit model markup to highlight non-compliant elements. How would you prompt the Agent?"

Challenge 2: Performance Optimization​

"The tool runs slowly on large models. What optimization would you request from the Agent?"

Challenge 3: Error Scenario​

"What happens when the Excel file format is invalid? Let's see how the Agent handles edge cases."

🚀 Advanced Agent Techniques​

Multi-Session Development​

SESSION 1: Core architecture and basic implementation
SESSION 2: Advanced features and optimization
SESSION 3: Testing and documentation
SESSION 4: Deployment and team training

Collaborative Agent Development​

DEVELOPER 1: Prompts for business logic
DEVELOPER 2: Prompts for UI/UX
DEVELOPER 3: Prompts for integration and testing
AGENT: Coordinates and maintains consistency

Agent-Driven Code Reviews​

PROMPT: "Review this implementation for potential issues:
- Security vulnerabilities
- Performance bottlenecks  
- Error handling gaps
- Code quality concerns"

📈 ROI Analysis​

Development Cost Savings​

  • Traditional Approach: 120 hours @ $150/hour = $18,000
  • Agent-Assisted Approach: 30 hours @ $150/hour = $4,500
  • Cost Savings: $13,500 (75% reduction)

Quality Improvements​

  • Reduced Bugs: 60% fewer issues in initial testing
  • Consistent Patterns: 90% reduction in code review iterations
  • Documentation: 100% documentation coverage vs. 40% traditional
  • Testing: Comprehensive test suite generated automatically

Time to Market​

  • Traditional Timeline: 3 weeks development + 1 week testing
  • Agent Timeline: 3 days development + 2 days testing
  • Acceleration: 80% faster time to market

🎤 Q&A Session Topics​

Technical Questions​

  • "How does Agent Mode handle complex business logic?"
  • "What are the limitations of autonomous development?"
  • "How do you ensure code security with AI-generated code?"

Process Questions​

  • "How do you establish coding standards for Agent development?"
  • "What's the best way to review AI-generated code?"
  • "How do you handle version control with Agent development?"

Strategic Questions​

  • "When should you use Agent Mode vs. traditional development?"
  • "How do you train teams on Agent-assisted development?"
  • "What's the future of autonomous software development?"

This demonstration showcases the cutting-edge capabilities of AI-assisted development, where agents can work autonomously for extended periods while maintaining professional code quality and following established best practices.

Next: Summary & Q&A - Wrap up and key takeaways