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+# Azure AI Agents JavaScript Demo - Development Guide
+
+## Project Structure
+
+This Azure AI Agents demo showcases different tooling capabilities through a console-based interface. The architecture follows a service-oriented pattern with clear separation of concerns:
+
+```
+src/
+├── index.ts                    # Entry point - Interactive console loop for demo selection
+├── types.ts                    # Central TypeScript definitions and interfaces
+├── config/
+│   ├── env.ts                  # Environment variable configuration and validation
+│   └── promptConfig.ts         # Agent configurations and prompt definitions
+├── services/
+│   ├── agentService.ts         # Core agent lifecycle management
+│   ├── threadService.ts        # Thread and message handling with streaming
+│   └── toolService.ts          # Tool factory and function execution
+└── utils/
+    ├── console.ts              # Interactive console utilities
+    └── formatting.ts           # String formatting helpers
+
+examples/                       # Individual SDK usage examples
+├── 1-basic.ts                  # Basic agent creation and deletion
+├── 2-threads.ts                # Thread management
+├── 3-messages.ts               # Message handling
+├── 4-streaming.ts              # Streaming responses
+└── 5-run.ts                    # Agent run execution
+
+files/                          # Sample data files for code interpreter demos
+```
+
+## Architecture Overview
+
+### Configuration-Driven Agent Creation
+Agents are defined as `PromptConfig` objects in `src/config/promptConfig.ts`:
+
+```typescript
+interface PromptConfig {
+    prompt: string;                    // User prompt/query
+    instructions?: string;             // Agent system instructions
+    emoji?: string;                    // Display emoji for console UI
+    tool?: "code-interpreter" | "function-tool" | "ai-search" | "bing-grounding";
+    filePath?: string;                 // File path for code-interpreter uploads
+    fileId?: string;                   // Runtime file ID tracking
+    executor?: any;                    // Function tool executor reference
+    tools?: ToolDefinition[];          // Azure AI tool definitions
+    toolResources?: any;               // Tool-specific resource configurations
+}
+```
+
+### Tool Factory Pattern
+`src/services/toolService.ts` implements a factory pattern for tool creation:
+- **code-interpreter**: File upload handling and Python code execution
+- **ai-search**: RAG functionality using Azure AI Search indexes
+- **function-tool**: Custom JavaScript functions via `FunctionToolExecutor`
+- **bing-grounding**: Real-time web data with connection configuration
+
+### Agent Lifecycle Management
+All agents follow this standardized lifecycle in `agentService.ts`:
+1. Create tools via `createTools()` factory
+2. Create agent with Azure AI Projects client
+3. Create thread and add user message
+4. Execute agent with streaming response handling
+5. **Always cleanup**: Delete uploaded files and agent resources
+
+## Coding Conventions
+
+### Module System
+- **ES Modules**: Project uses `"type": "module"` in package.json
+- **Import Extensions**: All TypeScript imports must end with `.js` extension
+- **File Paths**: Configuration file paths are relative to project root (`./files/`)
+
+### Naming Conventions
+- **Agent Names**: Follow pattern `agent-${selectedKey}` where selectedKey is the prompt configuration key
+- **File Naming**: camelCase for TypeScript files, kebab-case for configuration files
+- **Function Naming**: Descriptive camelCase with clear action verbs
+
+### Code Style
+- **TypeScript Strict Mode**: All code must pass strict TypeScript compilation
+- **Async/Await**: Prefer async/await over Promises for better readability
+- **Error Handling**: Use try/catch blocks with descriptive error messages
+- **Console Output**: Use emojis and structured formatting for user experience
+
+### Environment Configuration
+Required environment variables follow Azure AI Foundry patterns:
+```bash
+AI_FOUNDRY_PROJECT_ENDPOINT=https://<endpoint>.services.ai.azure.com/api/projects/<project>
+AI_MODEL=gpt-4o
+AI_SEARCH_CONNECTION_ID=/subscriptions/.../connections/<name>  # Optional
+AI_SEARCH_INDEX_NAME=<index-name>                              # Optional
+BING_GROUNDING_CONNECTION_ID=/subscriptions/.../connections/<name>  # Optional
+```
+
+### Azure SDK Integration
+- **Authentication**: Use `DefaultAzureCredential` for Azure resource access
+- **File Uploads**: Always use `assistants` purpose in `client.agents.files.upload()`
+- **Headers**: Include `x-ms-enable-preview: true` for preview features
+- **Resource Cleanup**: Always delete created agents and uploaded files
+
+## Testing Protocols
+
+### Build and Validation
+```bash
+# TypeScript compilation
+npm run build
+
+# Lint check (via TypeScript strict mode)
+npx tsc --noEmit
+```
+
+### Example Execution
+Individual SDK examples can be run to test specific functionality:
+```bash
+npm run basic        # Basic agent creation/deletion
+npm run threads      # Thread management
+npm run messages     # Message handling
+npm run streaming    # Streaming responses
+npm run run          # Complete agent run cycle
+```
+
+### Interactive Demo Testing
+```bash
+npm start           # Start interactive console demo
+# Test each agent type:
+# 1. Basic calculation (no tools)
+# 2. Function calling (CPU usage)
+# 3. Code generation (code interpreter)
+# 4. Data visualization (CSV analysis)
+# 5. Insurance coverage (AI Search)
+# 6. Stock market (Bing Grounding)
+```
+
+### Environment Testing
+Verify environment configuration before running:
+- Azure AI Foundry project endpoint accessibility
+- Model deployment availability
+- Optional service connections (AI Search, Bing Grounding)
+
+### Resource Cleanup Validation
+- Agents are properly deleted after execution
+- Uploaded files are removed from storage
+- No persistent resources remain after demo completion
+
+## Development Workflow
+
+### Setup
+1. Clone repository and install dependencies:
+   ```bash
+   npm install
+   ```
+
+2. Configure environment:
+   ```bash
+   cp .env.template .env
+   # Edit .env with your Azure AI Foundry details
+   ```
+
+3. Build project:
+   ```bash
+   npm run build
+   ```
+
+### Development Cycle
+1. **Code Changes**: Make modifications in `src/` directory
+2. **Build**: Run `npm run build` to compile TypeScript
+3. **Test**: Use `npm start` for interactive testing or individual examples
+4. **Validate**: Ensure resource cleanup and error handling work correctly
+
+### Adding New Agent Types
+1. Add new configuration to `src/config/promptConfig.ts`
+2. Extend tool factory in `src/services/toolService.ts` if needed
+3. Test with interactive demo and example scripts
+4. Ensure proper resource cleanup in disposal logic
+
+## Pull Request Guidelines
+
+### Code Quality Requirements
+- **TypeScript Compilation**: All code must compile without errors using `npm run build`
+- **Strict Mode Compliance**: Code must pass TypeScript strict mode checks
+- **Import Consistency**: Use `.js` extensions for all TypeScript imports
+- **Error Handling**: Include proper try/catch blocks and meaningful error messages
+
+### PR Description Format
+Include the following sections in your PR description:
+- **Summary**: Brief description of changes and motivation
+- **Testing**: How the changes were tested (build, examples, interactive demo)
+- **Resource Management**: Confirmation that resource cleanup works correctly
+- **Breaking Changes**: Any changes to public APIs or configuration format
+
+### Commit Message Standards
+- Use descriptive commit messages with action verbs
+- Include scope when relevant: `feat(agents): add new tool type`
+- Reference issue numbers: `fixes #123`
+
+### Review Checklist
+- [ ] Code compiles successfully with `npm run build`
+- [ ] Interactive demo works with new changes
+- [ ] Resource cleanup functions properly
+- [ ] Environment variables are documented if added
+- [ ] Error handling covers edge cases
+- [ ] Console output maintains user-friendly formatting
+
+### Azure-Specific Considerations
+- **Authentication**: Verify DefaultAzureCredential integration
+- **Service Connections**: Test optional service integrations gracefully fail when not configured
+- **API Compatibility**: Ensure compatibility with Azure AI Projects SDK versions
+- **Resource Limits**: Consider token usage and rate limiting implications
+
+## Common Integration Points
+
+### Azure AI Foundry
+- Project endpoint configuration and authentication
+- Model deployment access and token management
+- Preview feature enablement via request headers
+
+### File Management
+- Upload files with `assistants` purpose for code interpreter
+- Handle streaming file downloads for generated content
+- Implement proper cleanup to avoid storage costs
+
+### Tool Integration
+- Azure AI Search for RAG functionality with document indexing
+- Bing Grounding for real-time web data access
+- Function tools for custom JavaScript execution
+- Code interpreter for Python code execution and file generation
+
+### Error Scenarios
+- Network connectivity issues with Azure services
+- Missing or invalid authentication credentials
+- Tool-specific failures (search index unavailable, file upload errors)
+- Model or deployment availability issues
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