How Microsoft Developers Use AI in Real-World Coding

Session Date: May 20, 2025
Duration: 1 hour
Venue: Build 2025 Conference - BRK103
Speakers: David Fowler (Distinguished Engineer, Microsoft), Stephen Toub (Partner Software Engineer, Microsoft)
Link: [Microsoft Build 2025 Session BRK103]

AI in Real-World Development

Executive Summary

This session provides an authentic, practical look at how two Microsoft .NET developers integrate AI tools into their daily coding workflows. Through live demonstrations and real production examples, David Fowler and Stephen Toub showcase how GitHub Copilot and other AI tools have transformed their approach to ideation, code writing, testing, debugging, and maintenance—moving beyond “vibe coding” to pragmatic, production-ready development practices.

Key Topics Covered

🧠 1. AI for Ideation and Problem-Solving

The 10-Year Kestrel Bug Story

Real-World Challenge: A decade-old memory pool issue in Kestrel suddenly became critical for Azure App Service.

AI-Powered Solution Approach:

  • Rapid iteration through ideas - Four different implementation strategies in seconds
  • Implementation exploration - From basic limits to generational GC concepts
  • Context-driven discussions - AI as a collaborative partner, not just a code generator
  • Expert validation - Using AI insights to fuel productive conversations with team members

David’s Methodology:

Prompt Pattern: "Give me approaches, not answers"
├── Problem Context: Pool with concurrent queue, no limits
├── Request: Four ways to free memory  
├── Evaluation: Read suggestions with domain expertise
├── Iteration: Follow-up questions on promising approaches
└── Implementation: Transform ideas into production-ready code

Stephen’s Regex Optimization Journey

Technical Challenge: Improving .NET regex engine performance through literal optimizations.

AI Discovery Process:

  • Academic knowledge synthesis - AI identified optimization opportunities from literature
  • Concrete example requirements - Specificity needed for useful AI responses
  • Lookahead optimization discovery - AI identified unexplored optimization area
  • Production implementation - 10x performance improvement in .NET 10

Key Learning: AI excels at synthesizing existing knowledge and identifying patterns humans might miss.

📝 2. Code Documentation and Maintenance

Automated Comment Generation

The Documentation Challenge:

  • Legacy code understanding - 20-year-old regex interpreter with cryptic opcodes
  • Comment debt - 3,000 lines of undocumented C# code
  • Maintenance burden - Who wants to write extensive documentation?

AI-Powered Solutions:

Regex Interpreter Documentation:

// Before AI: "set counter null mark branch first for loop"
// After AI: "This implements a non-greedy branch for alternations and lazy quantifiers"

Model Context Protocol Library:

  • 6,000 lines of AI-generated XML comments - Reduced to 4,000 after human editing
  • Days of work compressed to hours - From documentation creation to refinement
  • Context-aware generation - AI understood method relationships and usage patterns

Table Generation and Technical Documentation

Practical Automation Examples:

  • XML table generation from switch statement analysis
  • API documentation with return type explanations
  • Cross-reference documentation maintaining consistency across large codebases

💻 3. Production Code Implementation

LINQ Shuffle Method (.NET 10)

Technical Innovation: New randomization method for IEnumerable sequences.

AI-Assisted Development Process:

  1. Algorithm identification - AI suggested reservoir sampling for .Shuffle().Take()
  2. Optimization discovery - Hyper-geometric distribution for .Shuffle().Take().Contains()
  3. Performance validation - 31μs → 4μs improvement with reduced allocations
  4. Code review assistance - AI caught missing cast in probability calculations

Real Performance Impact:

// Performance Results:
// Manual implementation: 31 microseconds
// Take() operator: 26 microseconds  
// Optimized Take().Contains(): 4 microseconds
// Allocation reduction: Significant memory savings

MemoryExtensions Overloads (40 Methods)

Scaling Code Generation:

  • Pattern completion - After implementing 2 methods, AI completed remaining 38
  • Test generation - Modified 2 tests, AI handled remaining 40 test variations
  • Tab-driven development - “Tabbed my way to glory” through repetitive implementations

🐛 4. Debugging and Issue Resolution

Named Pipes Memory Leak Case Study

The Critical Bug Report:

  • Partner team escalation - Memory leaks in HTTP client using named pipes
  • Immediate diagnosis - AI root-caused issue in 45 seconds
  • Solution identification - Missing disposal logic for outstanding operations

AI Debugging Workflow:

  1. Email copy-paste - Direct problem description to AI
  2. Context addition - System.IO.Pipes library scope
  3. Root cause analysis - AI identified exact method and issue
  4. Solution evaluation - Human judgment on AI’s proposed fixes

Time Savings: 30 minutes of investigation compressed to end-of-day quick fix.

GitHub Copilot Agent Integration

Automated Issue Resolution:

  • Issue assignment to AI - Direct GitHub integration
  • Autonomous PR creation - Fix implementation and test addition
  • Human validation - Expert review of AI-generated solution
  • Production deployment - Real fixes merged into .NET codebase

🧪 5. Testing and Quality Assurance

URI Template Testing Suite

Comprehensive Test Generation:

  • External test suite integration - 13-year-old standardized test repository
  • AI-generated test harness - Parse test files and validate against custom implementation
  • Validation logic - AI-generated code testing AI-generated formatting logic
  • Human orchestration - Developer ensuring correct integration

Edge Case Test Creation

Docker Compose Escape Sequence Example:

  • Complex string processing - Shell script escape handling with multiple quote types
  • Comprehensive edge cases - AI generated extensive test scenarios
  • Test-driven validation - AI created tests that initially failed its own implementation
  • Cross-model validation - Multiple LLMs couldn’t resolve conflicting test cases

🔧 6. Live Development Demonstration

Azure Aspire Repository Issue Resolution

Real-Time AI Development:

  • GitHub Codespaces integration - Cloud-based development environment
  • Issue context injection - Automatic problem understanding from GitHub issues
  • Multi-file modifications - Seven Azure resource files updated with consistent patterns
  • Documentation generation - Required XML comments added automatically
  • Test creation - Unit tests generated for new functionality

Development Workflow:

Issue Assignment → Copilot Agent Mode → Code Analysis → 
Implementation → Test Generation → PR Creation → Human Review

Live Demo Results:

  • Property exposure - Made private properties public across multiple resource types
  • Consistent documentation - XML comments required by repository standards
  • Test coverage - Unit tests for new functionality
  • Production-ready PR - Immediate merge candidate

Technical Architecture and Integration Patterns

AI Tool Integration Stack

Primary Tools Used:

  • GitHub Copilot - VS Code and Visual Studio integration
  • ChatGPT/Claude - Complex reasoning and ideation
  • Copilot Agent Mode - Autonomous development workflows
  • GitHub Codespaces - Cloud development with AI context

Development Methodology Evolution

Traditional Approach:

Problem → Research → Design → Implementation → Testing → Documentation

AI-Enhanced Approach:

Problem → AI Ideation → Expert Validation → AI Implementation → 
AI Testing → AI Documentation → Human Review → Production

Quality Assurance Patterns

AI-Human Collaboration Model:

  • AI handles repetitive tasks - Code generation, testing, documentation
  • Human provides expertise - Domain knowledge, architectural decisions, quality judgment
  • Iterative refinement - AI suggestions refined through human feedback
  • Context accumulation - Conversation history enables more sophisticated assistance

Productivity Impact Analysis

Quantified Benefits

Time Savings Examples:

  • 10-year bug resolution - Months of analysis compressed to days with AI ideation
  • Documentation creation - Days of work reduced to hours of editing
  • Test generation - 40 method tests automated after 2 manual examples
  • Debugging assistance - 30-minute investigations reduced to minutes

Quality Improvements:

  • Performance optimizations - AI identified academic algorithms for 10x speed improvements
  • Edge case coverage - Comprehensive test scenarios beyond human imagination
  • Consistency maintenance - Uniform patterns across large codebases
  • Knowledge synthesis - Academic research integrated into practical implementations

Scaling Development Efficiency

Individual Developer Impact:

  • Reduced cognitive load on repetitive tasks
  • Faster exploration of implementation alternatives
  • Automated quality assurance through test generation
  • Enhanced documentation maintenance

Team-Level Benefits:

  • Knowledge democratization - Junior developers access to expert-level assistance
  • Consistent code patterns across team members
  • Accelerated problem resolution through AI-assisted debugging
  • Reduced maintenance burden through automated documentation

Live Demo Insights

Real-World AI Development

Azure Aspire Issue Resolution:

  • GitHub integration - Seamless issue-to-code workflow
  • Multi-file consistency - Pattern application across similar files
  • Documentation automation - Required XML comments generated
  • Test creation - Appropriate unit test coverage

AI Agent Capabilities

Autonomous Development Features:

  • Context understanding - Issue requirements translated to implementation
  • Code pattern recognition - Consistent application across similar scenarios
  • Quality standards adherence - Repository requirements automatically followed
  • Human oversight integration - Review and approval checkpoints maintained

Session Highlights

“This is not about every single feature… This is about how we use particular tools.” - Stephen Toub

“Using it for ideation is a superpower. Being able to iterate, have conversations, dive into different implementations.” - David Fowler

“It’s automating these trivial things that I could do, I know how to do, but I don’t want to do. Just do it for me.” - Stephen Toub

“Rather than me taking days, what would have taken me literally days to write all of this documentation, it took me a few hours to do this editing.” - Stephen Toub

“This is where I just don’t want to spend time on… I can throw at these machines. It’s isolated. It’s a single function.” - David Fowler

Practical Implementation Guide

Getting Started Approach

Beginner AI Integration:

  1. Start with completions - Tab-based code generation in familiar contexts
  2. Explore chat interfaces - Use AI for ideation and problem discussion
  3. Automate documentation - Generate comments and technical documentation
  4. Enhance debugging - Use AI for error analysis and solution suggestions

Advanced AI Workflows:

  1. Agent mode adoption - Delegate complete feature implementation
  2. Multi-model strategy - Different models for different task types
  3. Context management - Maintain conversation history for sophisticated assistance
  4. Quality integration - AI-generated code with human review processes

Tool Selection Strategy

Task-Specific Model Usage:

  • Complex reasoning - Claude for deep analysis and architectural decisions
  • Code generation - GPT-4 for implementation and pattern application
  • Quick iterations - GitHub Copilot for real-time development assistance
  • Specialized tasks - Model experimentation for optimal results

Quality Assurance Framework

AI-Enhanced Development Process:

  1. Human expertise remains central to architectural decisions
  2. AI automation handles repetitive and well-understood tasks
  3. Iterative refinement through human feedback and validation
  4. Production standards maintained through review processes

Beyond “Vibe Coding”

Production-Ready AI Integration

What This Session Is NOT:

  • Speculative development - Not about throwing prompts at AI hoping for working code
  • Complete automation - Human expertise and judgment remain essential
  • Universal solution - Specific to .NET development contexts and experienced developers

What This Session IS:

  • Practical integration - Real workflows used by experienced Microsoft developers
  • Quality-focused - Production code standards maintained throughout AI assistance
  • Expertise-enhanced - AI amplifies human knowledge rather than replacing it
  • Iterative improvement - Continuous learning and adaptation of AI-assisted workflows

Future Implications

Evolution of Developer Roles

  • Orchestration focus - Developers become AI workflow orchestrators
  • Higher-level thinking - More time for architecture and design decisions
  • Quality assurance - Emphasis on review and validation skills
  • Continuous learning - Keeping pace with rapidly evolving AI capabilities

About the Speakers

David Fowler
Distinguished Engineer
Microsoft
16 years at Microsoft, creator of NuGet, SignalR, ASP.NET Core, and .NET Aspire. Architect of Azure SignalR Service, focused on simplifying microservices developer experiences.

Stephen Toub
Partner Software Engineer
Microsoft
Partner Software Engineer focusing on .NET performance and AI integration. Expert in runtime optimization and developer tooling enhancement.

This session demonstrates that AI integration in professional software development is not about replacing developer expertise, but about amplifying it—enabling experienced developers to tackle harder problems, move faster on routine tasks, and maintain higher quality standards through AI-assisted workflows.