🤖 Smart Sales Intelligence Agent

Capstone Project - AI Agents Intensive Course (Nov 2025)
Track: Enterprise Agents
Author: Harshith

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📋 Executive Summary

The Smart Sales Intelligence Agent is an enterprise multi-agent system that automates sales research and outreach, saving sales teams 10+ hours per week per prospect. By simply providing a company name, the system researches the company, identifies business challenges, finds decision-makers, and generates personalized outreach emails—all automatically.

Problem Statement

Sales teams waste countless hours manually researching prospects, analyzing their business needs, finding the right contacts, and crafting personalized outreach. This manual process is:

• ⏰ Time-intensive: 2-3 hours per prospect
• 📊 Inconsistent: Quality varies by researcher
• 🔄 Repetitive: Same research done multiple times
• 💰 Expensive: High-value sales time spent on research

Solution

An intelligent multi-agent system that automates the entire sales intelligence workflow through coordinated AI agents, each specialized in a specific task.

Value Delivered

• ⚡ 10+ hours saved per prospect researched
• 🎯 Higher quality insights through AI analysis
• 📈 Scalable to hundreds of prospects
• 💼 Consistent output quality every time

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🏗️ Architecture

Multi-Agent System Design

┌─────────────────────────────────────────────────────────┐
│                    USER INPUT                            │
│              "Company Name: Acme Corp"                   │
└─────────────────────┬───────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────────┐
│              ORCHESTRATOR AGENT                          │
│  • Coordinates all agents                                │
│  • Manages memory & state                                │
│  • Handles errors & logging                              │
└─────────────────────┬───────────────────────────────────┘
                      │
        ┌─────────────┴─────────────┐
        │  SEQUENTIAL AGENT FLOW     │
        └─────────────┬──────────────┘
                      │
                      ▼
        ┌─────────────────────────────────────────┐
        │ 1️⃣  RESEARCH AGENT                       │
        │  • Gathers company info                  │
        │  • Tools: Google Search                  │
        │  • Output: Company overview + news       │
        └─────────────┬───────────────────────────┘
                      │
                      ▼
        ┌─────────────────────────────────────────┐
        │ 2️⃣  ANALYSIS AGENT                       │
        │  • Analyzes business challenges          │
        │  • Tools: Gemini LLM                     │
        │  • Output: Challenges + opportunities    │
        └─────────────┬───────────────────────────┘
                      │
                      ▼
        ┌─────────────────────────────────────────┐
        │ 3️⃣  CONTACT AGENT                        │
        │  • Finds decision makers                 │
        │  • Tools: Search + Custom logic          │
        │  • Output: Prioritized contact list      │
        └─────────────┬───────────────────────────┘
                      │
                      ▼
        ┌─────────────────────────────────────────┐
        │ 4️⃣  OUTREACH AGENT                       │
        │  • Generates personalized emails         │
        │  • Tools: Gemini LLM                     │
        │  • Output: Ready-to-send emails          │
        └─────────────┬───────────────────────────┘
                      │
                      ▼
        ┌─────────────────────────────────────────┐
        │          FINAL OUTPUT                    │
        │  📊 Complete Intelligence Report         │
        │  • Company analysis                      │
        │  • Key challenges                        │
        │  • Decision maker contacts               │
        │  • Personalized email drafts             │
        └──────────────────────────────────────────┘

Cross-Cutting Concerns

• Memory Bank: Caches research to avoid redundant searches
• Session State: Maintains context across agent executions
• Observability: Comprehensive logging and tracing
• Error Handling: Graceful failure recovery

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✅ Required Features Implemented

This project demonstrates 6 out of 3 required capstone features:

1. ✅ Multi-Agent System - Sequential agent pipeline with 4 specialized agents
2. ✅ Tools - Google Search tool + custom business logic tools
3. ✅ Long-Term Memory - Memory Bank stores and retrieves past research
4. ✅ Session & State Management - SessionState maintains execution context
5. ✅ Observability - Comprehensive logging system with file and console output
6. ✅ Uses Gemini (Bonus!) - Analysis and Outreach agents powered by Gemini 2.0

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🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Google AI Studio API key (Get one here)

Installation

1. Clone the repository

git clone <your-repo-url>
cd sales-intelligence-agent

2. Create virtual environment

python3 -m venv venv
source venv/bin/activate  # On Mac/Linux
# OR
venv\Scripts\activate     # On Windows

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

# Copy the example env file
cp .env.example .env

# Edit .env and add your API key
GOOGLE_API_KEY=your_api_key_here

Running the Agent

Basic usage:

python main.py

The system will prompt you for a company name, then automatically:

1. Research the company
2. Analyze their business challenges
3. Find decision-makers
4. Generate personalized emails
5. Save a complete report

Output:

• Console: Real-time progress and summary
• reports/: JSON files with complete intelligence reports
• logs/: Detailed execution logs
• memory_bank.json: Cached research data

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📁 Project Structure

sales-intelligence-agent/
├── main.py                 # Main orchestrator
├── requirements.txt        # Python dependencies
├── .env                    # API keys (not committed)
├── .gitignore             # Git ignore rules
├── README.md              # This file
│
├── agents/                # Agent implementations
│   ├── __init__.py
│   ├── research_agent.py   # Gathers company data
│   ├── analysis_agent.py   # Analyzes challenges (Gemini)
│   ├── contact_agent.py    # Finds decision makers
│   └── outreach_agent.py   # Generates emails (Gemini)
│
├── tools/                 # Custom tools
│   ├── __init__.py
│   └── search_tool.py      # Google Search wrapper
│
├── utils/                 # Utilities
│   ├── __init__.py
│   ├── memory.py          # Memory Bank & Session State
│   └── logger.py          # Logging & observability
│
├── reports/               # Generated reports (created at runtime)
├── logs/                  # Execution logs (created at runtime)
└── memory_bank.json       # Research cache (created at runtime)

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🎯 Example Output

Input

Enter company name: Acme Corporation

Output Summary

📊 SALES INTELLIGENCE REPORT: Acme Corporation
================================================================

🏢 Company Overview:
   Industry: Technology/SaaS
   Size: 100-500 employees
   Location: San Francisco, CA

🎯 Key Challenges (3):
   1. Scaling infrastructure while maintaining performance
   2. Managing technical debt from rapid growth
   3. Integrating disparate systems and data sources

👥 Priority Contacts (3):
   • John Smith - Chief Technology Officer
   • Sarah Johnson - VP of Engineering
   • Michael Chen - Director of Product

📧 Outreach Emails Generated: 3

Sample Email

To: John Smith (Chief Technology Officer)
Subject: Helping Acme Corporation with Scaling infrastructure...

Hi John,

I noticed Acme Corporation has been experiencing rapid growth,
which is fantastic—but I imagine it's also creating some 
infrastructure challenges...

[Personalized content based on analysis]

Would you be open to a brief conversation about how we've 
helped similar companies scale efficiently?

Best regards,
[Your name]

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🔧 Configuration

Environment Variables (.env)

# Required
GOOGLE_API_KEY=your_api_key_here

# Optional (defaults provided)
MODEL_NAME=gemini-2.0-flash-exp
MAX_RETRIES=3
TIMEOUT_SECONDS=30

Customization

Change the Gemini model: Edit .env:

MODEL_NAME=gemini-2.0-flash-exp  # or gemini-pro, etc.

Adjust number of contacts: Edit agents/outreach_agent.py, line 45:

for contact in contacts[:3]:  # Change 3 to desired number

Modify analysis depth: Edit agents/analysis_agent.py, _create_analysis_prompt() method

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📊 Technical Highlights

Why Sequential Agents?

Each agent builds on the previous agent's output:

• Research → provides data for Analysis
• Analysis → informs Contact prioritization
• Contacts + Analysis → enable personalized Outreach

This sequential flow ensures each agent has the context it needs.

Memory Bank Benefits

• Avoids redundant API calls (saves time & money)
• Instant results for previously researched companies
• Persistent across sessions

Observability Implementation

Every agent action is logged with:

• Timestamp
• Agent name
• Input parameters
• Success/failure status
• Execution time

Logs are saved to logs/agent_YYYYMMDD.log

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🧪 Testing

Test with example companies

python main.py
# Enter: "Salesforce"
# Enter: "HubSpot"
# Enter: "Zoom"

Verify memory caching

# First run - will do full research
python main.py
# Enter: "TestCo"

# Second run - should use cache
python main.py
# Enter: "TestCo"
# Should see: "Found cached research for TestCo"

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🚀 Future Enhancements

Phase 2 Features:

• Real Google Custom Search API integration
• LinkedIn API for actual contact data
• Email sending capability (SendGrid/Gmail API)
• Web UI for easier interaction
• Multi-company batch processing
• CRM integration (Salesforce, HubSpot)
• A/B testing for email effectiveness

Deployment:

• Deploy to Google Cloud Run
• Add authentication
• Rate limiting for API calls
• Webhook support for automation

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📚 Key Learnings

What Worked Well

• Sequential agent architecture - each agent has clear responsibility
• Memory caching - dramatically improves performance on repeat queries
• Gemini integration - produces high-quality, contextual analysis and emails

Challenges Overcome

• Coordinating state between agents
• Error handling across the pipeline
• Balancing detail vs. conciseness in emails

Technical Decisions

• Why sequential vs parallel? - Each agent needs previous agent's output
• Why file-based memory? - Simple, portable, no database required for demo
• Why Gemini 2.0? - Fast, cost-effective, excellent at analysis & generation

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🤝 Contributing

This is a capstone project, but feedback is welcome!

To suggest improvements:

1. Open an issue describing the enhancement
2. Fork the repo
3. Create a feature branch
4. Submit a pull request

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📜 License

MIT License - Feel free to use this project as a starting point for your own agents.

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👨‍💻 About

Created for the Google AI Agents Intensive Course (Nov 2025)
Track: Enterprise Agents
Features: 6/3 required + bonus features

Contact

• GitHub: [your-github]
• LinkedIn: [your-linkedin]
• Email: [your-email]

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🙏 Acknowledgments

• Google AI & Kaggle for the excellent course
• Anthropic for ADK inspiration
• The AI Agents community for support

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⭐ If this project helped you, please star the repo!