Email Intelligence Agent is a modular, end-to-end system designed to extend traditional email analytics into an intelligent, context-aware automation platform.
Built on top of an ETL-based email analytics pipeline, this system integrates LLM-driven reasoning to:
- Classify email intent
- Detect urgency
- Extract structured entities
- Generate context-aware reply drafts
- Support human-in-the-loop approval
- Enable conditional automated responses
The platform is designed for extensibility toward fully agentic email automation in enterprise environments.
Traditional email systems provide storage and search but lack intelligence. Users manually:
- Classify and prioritize emails
- Identify actionable items
- Extract important information
- Draft repetitive responses
- Track conversation urgency
This project introduces an intelligence layer that automates understanding and response generation while maintaining user control.
- Microsoft Graph API for mailbox access
- Apache Airflow for ETL orchestration
- AWS S3 for raw email storage
- PostgreSQL for structured storage
- LLM-based intent classification
- Urgency scoring model
- Named entity extraction
- Conversation thread summarization
- Context memory handling
- Context-aware draft generation
- Human-in-the-loop approval workflow
- Conditional auto-reply logic
- Extensible tool-calling interface
Automatically categorizes emails into actionable types such as:
- Meeting requests
- Invoice or payment queries
- Support or issue reports
- Internal updates
- Escalations
- Sales or outreach
Computes a priority score based on:
- Keyword signals
- Sender importance
- Response latency
- Sentiment analysis
- Thread length
Extracts structured information including:
- Dates and deadlines
- Monetary amounts
- Invoice numbers
- Contact details
- Order identifiers
- Uses conversation history
- Maintains tone consistency
- Produces structured, professional drafts
- Allows manual review and editing
email-intelligence-agent/
│
├── agent/ # Intelligence modules
│ ├── classifier.py
│ ├── urgency.py
│ ├── entity_extractor.py
│ ├── reply_generator.py
│ └── memory.py
│
├── api/ # FastAPI orchestration layer
│ └── main.py
│
├── airflow_dags/ # ETL workflows
│ └── email_etl.py
│
├── database/ # Database schemas and models
│ └── schema.sql
│
├── ui/ # Streamlit interface
│ └── app.py
│
├── configs/ # Configuration files
│ └── config.py
│
├── requirements.txt
├── .env.example
└── README.md
Backend:
- Python
- FastAPI
- Apache Airflow
- PostgreSQL
Frontend:
- Streamlit
Storage:
- AWS S3
- PostgreSQL
Intelligence:
- LLM API integration
- Prompt engineering
- Modular agent design
Current Version: v0.1 (Foundation Setup)
Planned Implementation Phases:
Phase 1:
- Email classification
- Draft generation
- Approval interface
Phase 2:
- Tool-calling agent framework
- Conditional auto-send logic
- Escalation detection
Phase 3:
- Calendar integration
- CRM integration
- Fully autonomous conditional workflows
Clone the repository:
git clone <repository_url>
cd email-intelligence-agent
Create a virtual environment:
python -m venv venv
venv\Scripts\activate # Windows
source venv/bin/activate # macOS/Linux
Install dependencies:
pip install -r requirements.txt
Configure environment variables using .env.example.
- Read-only mailbox ingestion by default
- Draft-only response mode enabled initially
- Auto-send requires explicit configuration
- Full logging for audit traceability
- Human approval workflow before deployment
- Reinforcement learning for response optimization
- Conversation health scoring
- Multi-mailbox orchestration
- Role-based access control
- Enterprise-grade deployment configuration
Rishabh Venkataramanan
Email Intelligence and Agentic Automation System