Intelligent Customer Service System

A comprehensive, AI-powered customer service platform built with Claude 3 and advanced NLP capabilities. This system provides multilingual support, sentiment analysis, automated follow-ups, and detailed analytics for modern customer service operations.

🌟 Features

🤖 AI-Powered Agents

EnhancedCustomerServiceAgent

• Multilingual Support: Handles inquiries in 10+ languages
• Sentiment Analysis: Real-time emotion detection and response
• Knowledge Base Integration: Smart article search and suggestions
• Automated Follow-ups: Priority-based scheduling
• Satisfaction Surveys: Automated feedback collection
• Escalation Management: Multi-tier support system

CreativeAgent

• Content Generation: Poetry, stories, and creative writing
• Image Analysis: Visual content interpretation
• Emotional Intelligence: Sentiment-aware responses
• Style Adaptation: Multiple writing styles and tones

📊 Analytics & Visualization

• Sentiment Trends: Track customer satisfaction over time
• Language Distribution: Monitor language preferences
• Tool Usage Analytics: Optimize resource allocation
• Performance Metrics: Track response times and resolution rates

🔄 Automated Workflows

• Multi-step Processing: Seamless inquiry handling
• Priority Queue: Smart task management
• Error Recovery: Robust exception handling
• Audit Trails: Comprehensive logging

🚀 Getting Started

Prerequisites

# Python 3.8+ required
python -m pip install -r requirements.txt

Environment Setup

1. Create a .env file:

ANTHROPIC_API_KEY=your_api_key_here

2. Create required directories:

mkdir -p work_files/customer_service/analytics

Basic Usage

from src.agents.enhanced_customer_service_agent import EnhancedCustomerServiceAgent
from src.core.base import AgentSystem

# Initialize system
system = AgentSystem()

# Create agent
cs_agent = EnhancedCustomerServiceAgent(
    agent_id="cs_agent_1",
    work_dir="work_files/cs",
    api_keys={
        "anthropic": "your_api_key"
    }
)

# Register agent
system.register_agent(cs_agent)

# Process inquiry
result = await cs_agent.process_task(Task(
    task_id="inquiry_1",
    task_type="customer_inquiry",
    input_data="What's the status of my order?",
    parameters={"customer_id": "C1"}
))

Running Workflows

# Run enhanced customer service workflow
python examples/enhanced_customer_service_workflow.py

📖 Example Workflows

1. Basic Customer Service

# Process simple inquiry
inquiry = "What's the status of my order O2?"
result = await cs_agent.process_task(Task(
    task_id="basic_inquiry",
    task_type="customer_inquiry",
    input_data=inquiry
))

2. Multilingual Support

# Process Spanish inquiry
spanish_inquiry = "¿Cómo puedo devolver mi pedido?"
result = await cs_agent.process_task(Task(
    task_id="spanish_inquiry",
    task_type="customer_inquiry",
    input_data=spanish_inquiry
))

3. Knowledge Base Search

# Search articles
articles = await cs_agent.process_task(Task(
    task_id="kb_search",
    task_type="search_knowledge_base",
    input_data={
        "query": "return policy",
        "category": "returns"
    }
))

📊 Analytics Generation

The system automatically generates:

1. Sentiment Analysis:

   • Trend visualization
   • Emotional pattern detection
   • Priority alerts

2. Language Statistics:

   • Usage distribution
   • Translation metrics
   • Regional patterns

3. Performance Metrics:

   • Response times
   • Resolution rates
   • Tool efficiency

🛠 Advanced Configuration

Custom Knowledge Base

knowledge_base = {
    "returns": [
        {
            "id": "KB001",
            "title": "Return Policy",
            "content": "Our return policy..."
        }
    ]
}

cs_agent = EnhancedCustomerServiceAgent(
    # ... other params ...
    knowledge_base=knowledge_base
)

Custom Language Support

cs_agent = EnhancedCustomerServiceAgent(
    # ... other params ...
    supported_languages=["en", "es", "fr", "de", "it"]
)

📈 Visualization Examples

The system generates these visualizations:

1. sentiment_trends.png: Sentiment over time
2. language_distribution.png: Language usage
3. tool_usage.png: Tool utilization

🔍 Monitoring & Maintenance

Real-time Monitoring

# Get agent status
status = await cs_agent.handle_message(Message(
    sender="monitor",
    message_type="customer_service_status"
))

Cleanup Operations

# Process pending follow-ups
await cs_agent.process_followups()

# Archive old interactions
await cs_agent.cleanup()

🤝 Contributing

1. Fork the repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Create Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Claude 3 by Anthropic for AI capabilities
• NLTK for natural language processing
• Matplotlib and Seaborn for visualizations

📚 Documentation

Full documentation available in the /docs directory:

• agents.md: Detailed agent documentation
• workflows.md: Workflow examples
• api.md: API reference
• analytics.md: Analytics guide

🆘 Support

For support:

1. Check documentation
2. Search issues
3. Create new issue
4. Contact maintainers

🔮 Future Enhancements

1. Real-time Chat Integration:

   • WebSocket support
   • Chat interface
   • Mobile app integration

2. Advanced Analytics:

   • Predictive analytics
   • Customer behavior modeling
   • Trend forecasting

3. Enhanced Automation:

   • Workflow automation
   • Custom triggers
   • Integration webhooks

4. Security Features:

   • Role-based access
   • Audit logging
   • Data encryption

🎯 Agent Examples & Collaboration

EnhancedCustomerServiceAgent Examples

1. Order Management

# Handle order status inquiry
order_inquiry = await cs_agent.process_task(Task(
    task_id="order_status",
    task_type="customer_inquiry",
    input_data="Can you check the status of order O123?",
    parameters={"customer_id": "C1"}
))

# Process return request
return_request = await cs_agent.process_task(Task(
    task_id="return_request",
    task_type="customer_inquiry",
    input_data="I want to return my laptop, order number O456",
    parameters={
        "customer_id": "C2",
        "order_id": "O456"
    }
))

# Track shipment
tracking_inquiry = await cs_agent.process_task(Task(
    task_id="track_shipment",
    task_type="customer_inquiry",
    input_data="Where is my package? Tracking number: TRK789",
    parameters={"tracking_id": "TRK789"}
))

# Handle product complaint
complaint = await cs_agent.process_task(Task(
    task_id="product_complaint",
    task_type="customer_inquiry",
    input_data="My new phone isn't working properly",
    parameters={
        "customer_id": "C3",
        "priority": "high"
    }
))

# Process refund request
refund_request = await cs_agent.process_task(Task(
    task_id="refund_request",
    task_type="customer_inquiry",
    input_data="I'd like a refund for order O789",
    parameters={
        "customer_id": "C4",
        "order_id": "O789"
    }
))

CreativeAgent Examples

1. Story Generation

# Generate story from image
story = await creative_agent.process_task(Task(
    task_id="story_gen",
    task_type="generate_story",
    input_data={
        "image_url": "path/to/image.jpg",
        "style": "fantasy",
        "length": "medium"
    }
))

# Create character description
character = await creative_agent.process_task(Task(
    task_id="character_dev",
    task_type="create_character",
    input_data={
        "character_type": "protagonist",
        "genre": "sci-fi",
        "complexity": "high"
    }
))

# Generate poetry
poem = await creative_agent.process_task(Task(
    task_id="poetry_gen",
    task_type="generate_poetry",
    input_data={
        "theme": "nature",
        "style": "haiku",
        "mood": "peaceful"
    }
))

# Create scene description
scene = await creative_agent.process_task(Task(
    task_id="scene_desc",
    task_type="describe_scene",
    input_data={
        "image_url": "path/to/landscape.jpg",
        "perspective": "first_person",
        "tone": "mysterious"
    }
))

# Generate metaphors
metaphors = await creative_agent.process_task(Task(
    task_id="metaphor_gen",
    task_type="generate_metaphors",
    input_data={
        "concept": "time",
        "context": "philosophical",
        "count": 3
    }
))

🤝 Agent Collaboration

1. Customer Service with Creative Response

async def handle_creative_customer_service(
    system: AgentSystem,
    cs_agent: EnhancedCustomerServiceAgent,
    creative_agent: CreativeAgent,
    inquiry: str
) -> Dict[str, Any]:
    """Handle customer inquiry with creative response"""
    
    # First, process the customer inquiry
    cs_result = await cs_agent.process_task(Task(
        task_id="cs_inquiry",
        task_type="customer_inquiry",
        input_data=inquiry
    ))
    
    # If positive sentiment, generate creative response
    if cs_result.output["sentiment"]["compound"] > 0.5:
        creative_result = await creative_agent.process_task(Task(
            task_id="creative_response",
            task_type="generate_poetry",
            input_data={
                "theme": "customer_appreciation",
                "context": cs_result.output["response"]
            }
        ))
        
        return {
            "response": cs_result.output["response"],
            "creative_addition": creative_result.output["poem"]
        }
    
    return {"response": cs_result.output["response"]}

2. Multi-Agent Support Ticket

async def handle_complex_support_ticket(
    system: AgentSystem,
    cs_agent: EnhancedCustomerServiceAgent,
    creative_agent: CreativeAgent,
    ticket_data: Dict[str, Any]
) -> Dict[str, Any]:
    """Process complex support ticket with multiple agents"""
    
    # Get customer history
    history = await cs_agent.process_task(Task(
        task_id="get_history",
        task_type="get_customer_history",
        input_data={"customer_id": ticket_data["customer_id"]}
    ))
    
    # Generate empathetic response
    creative_response = await creative_agent.process_task(Task(
        task_id="empathy_response",
        task_type="generate_content",
        input_data={
            "tone": "empathetic",
            "context": ticket_data["issue"]
        }
    ))
    
    # Process technical solution
    solution = await cs_agent.process_task(Task(
        task_id="tech_solution",
        task_type="search_knowledge_base",
        input_data={"query": ticket_data["issue"]}
    ))
    
    return {
        "history": history.output,
        "empathetic_response": creative_response.output,
        "technical_solution": solution.output
    }

3. Visual Content Support

async def handle_visual_support(
    system: AgentSystem,
    cs_agent: EnhancedCustomerServiceAgent,
    creative_agent: CreativeAgent,
    image_url: str,
    inquiry: str
) -> Dict[str, Any]:
    """Handle support inquiry with visual content"""
    
    # Analyze image
    image_analysis = await creative_agent.process_task(Task(
        task_id="analyze_image",
        task_type="analyze_image",
        input_data={"image_url": image_url}
    ))
    
    # Generate visual description
    description = await creative_agent.process_task(Task(
        task_id="describe_image",
        task_type="describe_scene",
        input_data={
            "image_url": image_url,
            "context": inquiry
        }
    ))
    
    # Process customer inquiry with visual context
    support_response = await cs_agent.process_task(Task(
        task_id="visual_support",
        task_type="customer_inquiry",
        input_data=inquiry,
        parameters={
            "visual_context": image_analysis.output
        }
    ))
    
    return {
        "analysis": image_analysis.output,
        "description": description.output,
        "response": support_response.output
    }

4. Satisfaction Survey with Feedback

async def process_satisfaction_survey(
    system: AgentSystem,
    cs_agent: EnhancedCustomerServiceAgent,
    creative_agent: CreativeAgent,
    survey_response: Dict[str, Any]
) -> Dict[str, Any]:
    """Process customer satisfaction survey with creative feedback"""
    
    # Analyze survey sentiment
    sentiment = await cs_agent.process_task(Task(
        task_id="analyze_survey",
        task_type="analyze_sentiment",
        input_data={"text": survey_response["feedback"]}
    ))
    
    # Generate personalized response
    if sentiment.output["compound"] > 0:
        response = await creative_agent.process_task(Task(
            task_id="positive_feedback",
            task_type="generate_content",
            input_data={
                "tone": "grateful",
                "context": survey_response["feedback"]
            }
        ))
    else:
        response = await creative_agent.process_task(Task(
            task_id="improvement_feedback",
            task_type="generate_content",
            input_data={
                "tone": "apologetic",
                "context": survey_response["feedback"]
            }
        ))
    
    # Update customer record
    await cs_agent.process_task(Task(
        task_id="update_record",
        task_type="update_customer_record",
        input_data={
            "customer_id": survey_response["customer_id"],
            "survey_data": survey_response
        }
    ))
    
    return {
        "sentiment": sentiment.output,
        "response": response.output
    }

5. Knowledge Base Enhancement

async def enhance_knowledge_base(
    system: AgentSystem,
    cs_agent: EnhancedCustomerServiceAgent,
    creative_agent: CreativeAgent,
    article_data: Dict[str, Any]
) -> Dict[str, Any]:
    """Enhance knowledge base articles with creative content"""
    
    # Generate engaging title
    title = await creative_agent.process_task(Task(
        task_id="generate_title",
        task_type="generate_content",
        input_data={
            "type": "title",
            "context": article_data["content"]
        }
    ))
    
    # Create summary
    summary = await creative_agent.process_task(Task(
        task_id="create_summary",
        task_type="generate_content",
        input_data={
            "type": "summary",
            "content": article_data["content"]
        }
    ))
    
    # Add to knowledge base
    kb_entry = await cs_agent.process_task(Task(
        task_id="add_to_kb",
        task_type="update_knowledge_base",
        input_data={
            "title": title.output["title"],
            "content": article_data["content"],
            "summary": summary.output["summary"],
            "category": article_data["category"]
        }
    ))
    
    return {
        "title": title.output,
        "summary": summary.output,
        "kb_entry": kb_entry.output
    }

🔄 Running Multiple Agents

1. Initialize Agent System

# Create agent system
system = AgentSystem()

# Initialize agents
cs_agent = EnhancedCustomerServiceAgent(
    agent_id="cs_1",
    work_dir="work_files/cs",
    api_keys={"anthropic": "your_key"}
)

creative_agent = CreativeAgent(
    agent_id="creative_1",
    work_dir="work_files/creative",
    api_keys={"anthropic": "your_key"}
)

# Register agents
system.register_agent(cs_agent)
system.register_agent(creative_agent)

2. Create Workflow Manager

class WorkflowManager:
    def __init__(self, system: AgentSystem):
        self.system = system
        self.workflows = {
            "customer_service": handle_creative_customer_service,
            "support_ticket": handle_complex_support_ticket,
            "visual_support": handle_visual_support,
            "satisfaction_survey": process_satisfaction_survey,
            "kb_enhancement": enhance_knowledge_base
        }
    
    async def run_workflow(
        self,
        workflow_name: str,
        **kwargs
    ) -> Dict[str, Any]:
        if workflow_name not in self.workflows:
            raise ValueError(f"Unknown workflow: {workflow_name}")
        
        workflow = self.workflows[workflow_name]
        return await workflow(self.system, **kwargs)

3. Execute Workflows

# Initialize workflow manager
workflow_manager = WorkflowManager(system)

# Run customer service workflow
result = await workflow_manager.run_workflow(
    "customer_service",
    inquiry="I need help with my order",
    customer_id="C1"
)

# Run visual support workflow
visual_result = await workflow_manager.run_workflow(
    "visual_support",
    image_url="path/to/image.jpg",
    inquiry="What's wrong with my product?"
)

4. Monitor Performance

# Get agent status
cs_status = await cs_agent.handle_message(Message(
    sender="monitor",
    message_type="customer_service_status"
))

creative_status = await creative_agent.handle_message(Message(
    sender="monitor",
    message_type="creative_status"
))

# Process results
print(f"Customer Service: {cs_status}")
print(f"Creative Agent: {creative_status}")

5. Cleanup Resources

# Cleanup agents
await cs_agent.cleanup()
await creative_agent.cleanup()

# Close system
await system.shutdown()

These examples demonstrate how to:

1. Use each agent independently
2. Combine agents for complex tasks
3. Create reusable workflows
4. Monitor agent performance
5. Manage system resources

Each workflow shows a different aspect of agent collaboration:

• Customer service with creative responses
• Complex support ticket handling
• Visual content analysis and support
• Survey processing with feedback
• Knowledge base enhancement

The WorkflowManager provides a structured way to:

• Organize multiple workflows
• Handle dependencies between agents
• Monitor execution
• Manage resources
• Scale the system

🤖 Flexible Agents System

A comprehensive, AI-powered multi-agent system built with Claude 3, featuring intelligent agents for customer service, content creation, data processing, and more. This system demonstrates advanced agent collaboration and task processing capabilities.

🌟 System Overview

Core Components

1. Agent System

   • Task distribution
   • Message routing
   • Resource management
   • Error handling
   • Performance monitoring

2. Base Agent

   • Async task processing
   • Message handling
   • Tool integration
   • State management
   • Error recovery

🎯 Available Agents

1. 🎭 EnhancedCustomerServiceAgent

Advanced customer service handling with multi-language support and sentiment analysis.

Capabilities:

• Multi-language support (10+ languages)
• Real-time sentiment analysis
• Automated follow-ups
• Knowledge base integration
• Satisfaction surveys
• Escalation management

Example Usage:

cs_agent = EnhancedCustomerServiceAgent(
    agent_id="cs_1",
    work_dir="work_files/cs",
    api_keys={"anthropic": "your_key"}
)

# Handle customer inquiry
result = await cs_agent.process_task(Task(
    task_type="customer_inquiry",
    input_data="What's my order status?",
    parameters={"order_id": "O123"}
))

2. 📝 CreativeAgent

Content generation and creative writing with visual capabilities.

Capabilities:

• Story generation
• Poetry creation
• Character development
• Scene description
• Metaphor generation
• Image-based content

Example Usage:

creative_agent = CreativeAgent(
    agent_id="creative_1",
    work_dir="work_files/creative",
    api_keys={"anthropic": "your_key"}
)

# Generate story from image
story = await creative_agent.process_task(Task(
    task_type="generate_story",
    input_data={
        "image_url": "path/to/image.jpg",
        "style": "fantasy"
    }
))

3. 🖼️ ImageAgent

Image processing and analysis capabilities.

Capabilities:

• Image analysis
• Visual content generation
• Object detection
• Scene understanding
• Style transfer
• Image comparison

Example Usage:

image_agent = ImageAgent(
    agent_id="image_1",
    work_dir="work_files/image",
    api_keys={"anthropic": "your_key"}
)

# Analyze image
analysis = await image_agent.process_task(Task(
    task_type="analyze_image",
    input_data={"image_url": "path/to/image.jpg"}
))

4. 🔄 TranslationAgent

Advanced language translation and localization.

Capabilities:

• Multi-language translation
• Context preservation
• Style adaptation
• Idiom handling
• Cultural adaptation
• Quality assurance

Example Usage:

trans_agent = TranslationAgent(
    agent_id="trans_1",
    work_dir="work_files/translation",
    api_keys={"anthropic": "your_key"}
)

# Translate text
translation = await trans_agent.process_task(Task(
    task_type="translate",
    input_data={
        "text": "Hello, world!",
        "target_language": "es"
    }
))

5. 📊 DataProcessor

Data analysis and transformation capabilities.

Capabilities:

• Data cleaning
• Format conversion
• Statistical analysis
• Pattern recognition
• Data visualization
• Anomaly detection

Example Usage:

data_agent = DataProcessor(
    agent_id="data_1",
    work_dir="work_files/data",
    api_keys={"anthropic": "your_key"}
)

# Process dataset
result = await data_agent.process_task(Task(
    task_type="analyze_data",
    input_data={
        "file_path": "data.csv",
        "analysis_type": "statistical"
    }
))

🤝 Agent Collaboration

1. Customer Service Enhancement

async def enhanced_customer_service(
    system: AgentSystem,
    inquiry: str,
    image_url: str = None
) -> Dict[str, Any]:
    """Handle customer inquiry with multiple agents"""
    
    # Initialize agents
    cs_agent = system.get_agent("customer_service")
    creative_agent = system.get_agent("creative")
    image_agent = system.get_agent("image")
    
    results = {}
    
    # Process image if provided
    if image_url:
        image_analysis = await image_agent.process_task(Task(
            task_type="analyze_image",
            input_data={"image_url": image_url}
        ))
        results["image_analysis"] = image_analysis.output
    
    # Handle customer inquiry
    cs_result = await cs_agent.process_task(Task(
        task_type="customer_inquiry",
        input_data=inquiry,
        parameters={"image_context": results.get("image_analysis")}
    ))
    results["cs_response"] = cs_result.output
    
    # Generate creative response if positive sentiment
    if cs_result.output["sentiment"]["compound"] > 0.5:
        creative_result = await creative_agent.process_task(Task(
            task_id="creative_response",
            task_type="generate_poetry",
            input_data={
                "theme": "customer_appreciation",
                "context": cs_result.output["response"]
            }
        ))
        results["creative_response"] = creative_result.output
    
    return results

2. Multi-language Content Creation

async def create_multilingual_content(
    system: AgentSystem,
    content_request: Dict[str, Any]
) -> Dict[str, Any]:
    """Create content in multiple languages"""
    
    # Initialize agents
    creative_agent = system.get_agent("creative")
    trans_agent = system.get_agent("translation")
    
    # Generate base content
    content = await creative_agent.process_task(Task(
        task_type="generate_content",
        input_data=content_request
    ))
    
    # Translate to target languages
    translations = {}
    for lang in content_request["target_languages"]:
        trans_result = await trans_agent.process_task(Task(
            task_type="translate",
            input_data={
                "text": content.output["content"],
                "target_language": lang
            }
        ))
        translations[lang] = trans_result.output
    
    return {
        "original": content.output,
        "translations": translations
    }

🔄 Workflow Examples

1. Customer Support Workflow

# Initialize system
system = AgentSystem()

# Register agents
system.register_agent(EnhancedCustomerServiceAgent(...))
system.register_agent(CreativeAgent(...))
system.register_agent(ImageAgent(...))

# Create workflow
workflow = CustomerSupportWorkflow(system)

# Process customer inquiry
result = await workflow.process_inquiry(
    text="My product is damaged",
    image="damage.jpg"
)

2. Content Creation Workflow

# Initialize system
system = AgentSystem()

# Register agents
system.register_agent(CreativeAgent(...))
system.register_agent(ImageAgent(...))
system.register_agent(TranslationAgent(...))

# Create workflow
workflow = ContentCreationWorkflow(system)

# Generate multi-language content
content = await workflow.create_content(
    type="blog_post",
    topic="AI Trends",
    languages=["en", "es", "fr"]
)

📊 Performance Monitoring

# Get agent statistics
stats = await system.get_statistics()

# Monitor specific agent
agent_stats = await cs_agent.get_performance_metrics()

# Generate system report
report = await system.generate_report()

🛠️ Advanced Configuration

1. Custom Knowledge Base

knowledge_base = {
    "products": [...],
    "policies": [...],
    "faqs": [...]
}

cs_agent = EnhancedCustomerServiceAgent(
    knowledge_base=knowledge_base,
    ...
)

2. Custom Workflows

class CustomWorkflow(BaseWorkflow):
    async def process(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        # Custom workflow logic
        pass

🔍 Error Handling

try:
    result = await agent.process_task(task)
except AgentError as e:
    # Handle agent-specific errors
    logger.error(f"Agent error: {e}")
except SystemError as e:
    # Handle system-level errors
    logger.error(f"System error: {e}")

📈 Scaling

1. Horizontal Scaling

# Create agent pool
pool = AgentPool(
    agent_class=EnhancedCustomerServiceAgent,
    pool_size=5
)

# Process tasks
results = await pool.process_batch(tasks)

2. Load Balancing

# Create load balancer
balancer = LoadBalancer(
    strategy="round_robin",
    agents=[agent1, agent2, agent3]
)

# Process task
result = await balancer.process_task(task)

🔒 Security

1. API Key Management

from utils.security import KeyManager

key_manager = KeyManager()
api_keys = key_manager.get_keys()

2. Access Control

@requires_permission("admin")
async def sensitive_operation():
    pass

📚 Resources

• Full Documentation
• API Reference
• Example Collection
• Contributing Guide

🤝 Contributing

1. Fork the repository
2. Create feature branch
3. Make changes
4. Run tests
5. Submit pull request

📝 License

MIT License - see LICENSE file for details

Specialized Workflows

1. Employee Offboarding

Located in examples/employee_offboarding_workflow.py, this workflow automates the complete employee offboarding process:

• Disables user account and sets out-of-office message
• Removes licenses
• Sets up email forwarding to manager
• Removes from groups and teams
• Wipes company devices
• Archives data
• Generates comprehensive offboarding report

2. Security Audit

Located in examples/security_audit_workflow.py, this workflow performs comprehensive security audits:

• User audit (inactive accounts, MFA status, admin accounts)
• Device audit (compliance, encryption, updates)
• Data audit (external sharing, DLP violations)
• Automated remediation based on findings
• Configurable notifications and reporting

3. Compliance Monitoring

Located in examples/compliance_monitoring_workflow.py, this workflow ensures continuous compliance:

• Data classification and sensitivity scanning
• Policy compliance monitoring
• Access reviews (privileged roles, guest access)
• Automated remediation actions
• Compliance reporting and alerts

Configuration Templates

1. M365 Configuration

Located in config/templates/m365_config_template.json:

• Authentication settings
• Environment configuration
• Logging preferences
• Agent-specific settings
• Workflow configurations

2. Workflow Templates

Located in config/templates/workflow_templates/:

Security Audit Workflow

• Audit components and checks
• Thresholds and severity levels
• Notification settings
• Remediation actions
• Reporting preferences

Compliance Workflow

• Data classification settings
• Policy compliance rules
• Access review scope
• Alert configuration
• Automation settings

Agents

1. M365 Admin Agent

Core agent for Microsoft 365 administration:

• User management
• License management
• Group management
• Security management
• Reporting capabilities

2. Intune Agent

Device management specialist:

• Device compliance
• Policy management
• App management
• Configuration profiles

3. Exchange Agent

Email and calendar management:

• Mailbox configuration
• Calendar management
• Distribution groups
• Mail flow rules

4. Teams Agent

Teams workspace management:

• Team/channel management
• Meeting policies
• App management
• Security settings

5. Bookings Agent

Appointment and service management:

• Business management
• Staff management
• Service configuration
• Appointment handling
• Customer management

Getting Started

1. Copy the appropriate configuration template:

cp config/templates/m365_config_template.json config/m365_config.json

2. Update the configuration with your credentials:

• Tenant ID
• Client ID
• Client Secret
• Environment settings

3. Choose a workflow to run:

# Employee offboarding
python examples/employee_offboarding_workflow.py

# Security audit
python examples/security_audit_workflow.py

# Compliance monitoring
python examples/compliance_monitoring_workflow.py

Best Practices

1. Security:

• Store credentials securely
• Use least-privilege access
• Enable audit logging
• Regular security reviews

2. Compliance:

• Regular compliance scans
• Document all changes
• Maintain audit trails
• Review access regularly

3. Automation:

• Test workflows in staging
• Monitor automated actions
• Set up alerts for failures
• Regular backup checks

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

MIT License - see LICENSE file for details