CFO Financial Insight Dashboard — AI-Driven Financial Engineering Agentic System

Overview

This repository provides a structured framework for an AI-driven financial engineering and market analysis agentic system using Azure AI Agent Service SDK / Microsoft Agent Framework. It includes:

A Gradio dashboard that blends SAP (ES5 demo) data, simulated Salesforce pipeline, and business news
Multi-agent architecture for CFO-ready insights and strategy options
Structured Python project with required dependencies
Configuration file templates (.env)
Step-by-step setup guide

🤖 AI Agents Overview - Microsoft Agent Framework with Azure OpenAI

The system incorporates multiple AI-driven agents specialized in different aspects of financial and market analysis:

Behind the scene multi-agent orchestration in play:

Agent Architecture

Agent Name	Role
🌐 market-intelligence-agent	Analyzes market trends, news sentiment, customer pipeline, and currency exposure
💰 finance-pulse-agent	Evaluates SAP financial health, customer/product signals, and performance metrics
⚠️ risk-monitor-agent	Surfaces liquidity, compliance, and volatility risks across the dataset
📊 cfo-strategist	Fuses specialist reports into three actionable strategy options
📋 board-briefing-agent	Produces a board-ready narrative with executive summary

Agent Flows

Market Analysis Agent → Analyzes market trends, customer behavior, geographic demand
Financial Analysis Agent → Evaluates financials, balance sheets, cost efficiency
Risk Monitor Agent → Identifies compliance, liquidity, and exposure risks
Strategic Planning Agent → Develops three unique business strategies with implementation roadmaps
Board Briefing Agent → Creates executive-ready narrative for board distribution

📂 Project Structure

📦 Stock-Analysis-AutoGen-Multi-Agent
 ┣ 📜 sap-finance-analysis-withUI-8-FINAL-ES5-FINAL_AgentFramework.py  # Main dashboard app
 ┣ 📜 requirements.txt  # Dependencies
 ┣ 📜 .env.example  # Environment variables template
 ┣ 📜 README-financeagents.md  # This documentation
 ┗ 📜 LICENSE  # License file

🔧 Prerequisites

OS: Windows / macOS / Linux
Python: 3.10 or newer
Graphviz: Required for the flow diagram (dot must be on PATH)
Azure OpenAI: Chat deployment (GPT-4 recommended)
Azure Logic App: For email automation (see setup below)
Optional: Currents API Key for latest news
SAP ES5 demo credentials (or your SAP OData endpoint)

🔧 Setup Instructions

1️⃣ Clone Repository

git clone https://github.com/amitlals/Stock-Analysis-AutoGen-Multi-Agent.git
cd Stock-Analysis-AutoGen-Multi-Agent

2️⃣ Setup Python Virtual Environment

python -m venv venv

# For Mac/Linux
source venv/bin/activate

# For Windows PowerShell
.\venv\Scripts\Activate

3️⃣ Install Dependencies

pip install -U pip setuptools wheel
pip install -r requirements.txt

If requirements.txt is missing, install core libs:

pip install gradio plotly pandas requests python-dotenv graphviz pydantic urllib3 openai

4️⃣ Microsoft Agent Framework Dependency

⚠️ Note: This version uses agent-framework, a Microsoft internal/preview SDK.

Option A: If you have access to the package:

pip install agent-framework

Option B: If agent-framework is unavailable:

See the standard OpenAI SDK version which uses the openai package directly:

pip install openai

5️⃣ Install Graphviz (for flow diagrams)

Windows: Download from graphviz.org and add to PATH
macOS: brew install graphviz
Linux: sudo apt-get install graphviz

6️⃣ Set Up Environment Variables

Create a .env file in the project root:

# .env

# SAP Connection
SAP_USERNAME=your_sap_user
SAP_PASSWORD=your_sap_password
SAP_BASE_URL=https://sapes5.sapdevcenter.com/sap/opu/odata/IWBEP/GWSAMPLE_BASIC
SAP_CLIENT=002

# News API (optional; if unset, news is skipped)
CURRENTS_API_KEY=your_currents_api_key

# Azure OpenAI / Microsoft Agent Framework
API_KEY=your_azure_openai_api_key
AZURE_ENDPOINT=https://your-resource-name.openai.azure.com
MODEL_DEPLOYMENT_NAME=gpt-4
MODEL_API_VERSION=2024-10-21

# Email dispatch via Logic App (Options tab)
LOGICAPP_ENDPOINT=https://your-logicapp-url
EMAIL_RECIPIENT=recipient@example.com

📧 Azure Logic App Setup (Email Automation)

The dashboard uses an Azure Logic App to send strategy option emails. Follow these steps to create and configure it:

1️⃣ Create a New Logic App

Go to Azure Portal
Click Create a resource → Search for Logic App
Click Create and fill in:
- Subscription: Your Azure subscription
- Resource Group: Create new or select existing
- Logic App name: cfo-email-dispatcher (or your choice)
- Region: Select your preferred region
- Plan type: Consumption (pay-per-execution) recommended for testing
Click Review + create → Create
Wait for deployment, then click Go to resource

2️⃣ Design the Logic App Workflow

In your Logic App, click Logic app designer (or Edit if prompted)
Select Blank Logic App template

Step A: Add HTTP Trigger

Search for HTTP → Select When a HTTP request is received
Click Use sample payload to generate schema

Paste this sample JSON:

{
  "Recipient": "user@example.com",
  "Subject": "CFO Strategy Pick: Option 1",
  "Body": "<html><body><h1>Strategy Details</h1></body></html>"
}

Click Done — the schema auto-generates
Set Method to POST

Step B: Add Send Email Action

Option A: Using Office 365 Outlook

Click + New step
Search for Office 365 Outlook → Select Send an email (V2)
Sign in with your Microsoft 365 account when prompted
Configure the action:
- To: Click in field → Dynamic content → Select Recipient
- Subject: Click in field → Dynamic content → Select Subject
- Body: Click in field → Dynamic content → Select Body
- Toggle Is HTML to Yes

Option B: Using SendGrid (Alternative)

Click + New step
Search for SendGrid → Select Send email (V4)
Connect with your SendGrid API key
Configure similarly with dynamic content

Option C: Using Gmail

Click + New step
Search for Gmail → Select Send email (V2)
Sign in with your Google account
Configure with dynamic content

Step C: Add Response Action

Click + New step
Search for Response → Select Response
Configure:
- Status Code: 200
- Body: {"status": "Email sent successfully"}

3️⃣ Save and Get the Endpoint URL

Click Save in the designer toolbar
Go back to the HTTP trigger step

Copy the HTTP POST URL — it looks like:

https://prod-xx.eastus.logic.azure.com:443/workflows/abc123.../triggers/manual/paths/invoke?api-version=2016-10-01&sp=%2Ftriggers...

Add this URL to your .env file:

LOGICAPP_ENDPOINT=https://prod-xx.eastus.logic.azure.com:443/workflows/...

4️⃣ Complete Logic App Workflow Diagram

┌─────────────────────────────────────────────────────────┐
│  When a HTTP request is received                        │
│  ├─ Method: POST                                        │
│  └─ Schema: { Recipient, Subject, Body }                │
└─────────────────────┬───────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────────┐
│  Send an email (V2) - Office 365 Outlook                │
│  ├─ To: @{triggerBody()?['Recipient']}                  │
│  ├─ Subject: @{triggerBody()?['Subject']}               │
│  ├─ Body: @{triggerBody()?['Body']}                     │
│  └─ Is HTML: Yes                                        │
└─────────────────────┬───────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────────┐
│  Response                                               │
│  ├─ Status Code: 200                                    │
│  └─ Body: {"status": "Email sent successfully"}         │
└─────────────────────────────────────────────────────────┘

5️⃣ Test the Logic App

From Azure Portal:

Click Run Trigger → Run with payload

Enter test JSON:

{
  "Recipient": "your-email@example.com",
  "Subject": "Test Email from Logic App",
  "Body": "<h1>Hello!</h1><p>This is a test.</p>"
}

Click Run and check your inbox

From Command Line (curl):

curl -X POST "YOUR_LOGIC_APP_URL" \
  -H "Content-Type: application/json" \
  -d '{"Recipient":"your-email@example.com","Subject":"Test","Body":"<p>Test</p>"}'

From PowerShell:

$body = @{
    Recipient = "your-email@example.com"
    Subject = "Test Email"
    Body = "<h1>Test</h1>"
} | ConvertTo-Json

Invoke-RestMethod -Uri "YOUR_LOGIC_APP_URL" -Method Post -Body $body -ContentType "application/json"

6️⃣ Troubleshooting Logic App

Issue	Solution
HTTP 400 Bad Request	Check JSON schema matches exactly: `Recipient`, `Subject`, `Body` (case-sensitive)
HTTP 401 Unauthorized	Regenerate the trigger URL or check access policies
HTTP 404 Not Found	Verify the Logic App URL is correct and the app is enabled
Email not received	Check spam folder; verify Office 365 connection is authorized
Connection expired	Go to Logic App → API Connections → Reauthorize the email connector

7️⃣ Security Best Practices

⚠️ Never commit the Logic App URL to version control — treat it as a secret
🔒 Consider adding IP restrictions in Logic App settings for production
🔑 Use Azure Key Vault to store the endpoint URL in production environments
📋 Enable diagnostic logging in Logic App for monitoring

📜 Azure AI Agent Service SDK Setup

1️⃣ Install Azure SDK

pip install azure-ai-agent-sdk

2️⃣ Authenticate and Set Up Azure Service

# Log in to Azure CLI
az login

# Set your subscription
az account set --subscription "<your-subscription-id>"

# Deploy AI Agent Service (optional)
az ai agent create --name StockAnalysisAgent --resource-group your-resource-group --location eastus --sku Standard

3️⃣ Retrieve API Key

Store your Azure OpenAI API Key in .env as API_KEY.

🚀 Running the Project

python sap-finance-analysis-withUI-8-FINAL-ES5-FINAL_AgentFramework.py

The app launches at http://127.0.0.1:7860

Dashboard Tabs

Tab	Description
Performance & Analysis	KPIs, SAP/SF charts, headline table, AI insights
Strategic Options & Dispatch	AI-generated options + email dispatch via Logic App
System Flow	Click to generate agents data-flow diagram (Graphviz)

What Happens on Refresh

✅ Connects to SAP ES5 & fetches orders/products data
✅ Retrieves market & financial news
✅ Generates simulated Salesforce pipeline data
✅ Processes data into structured insights
✅ Utilizes 5 AI agents for market, financial & risk analysis
✅ Generates three strategic recommendations
✅ Produces board-ready narrative

Email Dispatch Flow

Select a strategy option (Option 1, 2, or 3)
Click Confirm & Dispatch
The app sends a POST request to your Logic App
Logic App sends a formatted HTML email to the configured recipient
Confirmation message appears in the dashboard

✅ Requirements (`requirements.txt`)

# Core dependencies
requests>=2.31.0
urllib3>=2.0.0
python-dotenv>=1.0.0

# UI Framework
gradio>=4.0.0

# Data & Visualization
pandas>=2.0.0
plotly>=5.18.0
pydantic>=2.0.0

# Diagram Generation
graphviz>=0.20.0

# Azure OpenAI
openai>=1.3.0

# Microsoft Agent Framework (if available)
# agent-framework>=0.1.0

🛠️ Agent Framework Behavior (Structured Output)

The code uses Microsoft Agent Framework (agent-framework) with AzureOpenAIChatClient
Because some Azure chat models do not support JSON schema response_format, agents are prompted to reply with raw JSON only
A tolerant parser:
- Extracts JSON from code fences or prose
- Unwraps extra root keys
- Coerces fields (severity, horizon, confidence)
- Fills defaults where needed
Specialist outputs are merged by the cfo-strategist into StrategyResponseModel
The board-briefing-agent produces a BoardNarrativeModel

🔌 SAP Connectivity Notes

The demo endpoint often omits a CSRF token for GETs
Warnings like "Proceeding to fetch … without CSRF token" are expected for read-only calls
If needed:
- Confirm SAP_BASE_URL and SAP_CLIENT are correct
- Increase timeouts or disable SSL verification only in trusted/dev environments

🐛 Troubleshooting

Issue	Solution
HTTP 400: Invalid parameter 'response_format'	Ensure you're running the updated v8 AgentFramework file
Non-structured payload warnings	Reduce agent temperature (0.05–0.2) or tighten prompts
Pandas deprecation warning for 'M'	Addressed by using 'ME' (month-end) in `simulate_sales_trend`
Graphviz error (dot not found)	Install Graphviz and ensure `dot` is on your PATH
SAP CSRF warnings	Safe to ignore if data loads; expected for read-only demo calls
agent-framework not found	Use the standard `openai` package version instead
Email dispatch failed	Check Logic App URL, verify JSON schema, test Logic App directly

⚙️ Configuration Tips

Model selection: Use GPT-4 or capable Azure OpenAI chat model for reasoning and JSON generation
Temperature: If structured responses are inconsistent, lower temperature (0.05–0.2)
Environment isolation: Keep this project in a clean venv to avoid library conflicts
Logs: Review console logs for agent prompts/issues and SAP fetch diagnostics

🔧 Extending the Agents

Add new agents: Mirror the specialist agent pattern; return AgentInsightReport JSON
Connect real Salesforce/ERP: Replace simulated fetch with your API calls
Adjust UI: Modify the option renderer to emphasize owner or confidence thresholds

🔒 Security

⚠️ Do not commit .env files — Treat API keys and credentials as secrets
⚠️ Do not commit Logic App URLs — They contain embedded authentication
⚠️ Scrub sensitive data before exporting or sharing screenshots
⚠️ Add .env to .gitignore

📋 Quick Commands (Windows PowerShell)

python -m venv venv
.\venv\Scripts\Activate
pip install -U pip setuptools wheel
pip install -r requirements.txt
pip install gradio plotly pandas requests python-dotenv graphviz pydantic openai
python sap-finance-analysis-withUI-8-FINAL-ES5-FINAL_AgentFramework.py

🏁 Next Steps

Enhance AI Agents: Add working-capital or liquidity agents for deeper analysis
Expand Data Sources: Integrate stock market APIs for real-time analytics
Deploy on Azure: Use Azure Functions or containerized service for production
Connect Real Systems: Replace simulated Salesforce with actual CRM integration
Add Approval Workflow: Extend Logic App with Microsoft Teams approval before email

📝 Citation

If you use this project in your research or work, please cite:

@article{lal2025aiagents,
  title={A Survey of AI Agent Architectures in Enterprise SAP Ecosystems},
  author={Lal, Amit},
  year={2025},
  publisher={TechRxiv},
  note={Preprint}
}

@article{lal2025rpt1,
  title={Evaluating SAP RPT-1 for Enterprise Business Process Prediction},
  author={Lal, Amit},
  year={2025},
  publisher={TechRxiv},
  note={Preprint}
}

@book{lal2024ai2040,
  title={AI in 2040},
  author={Lal, Amit},
  year={2024},
  isbn={9798346459941}
}

📜 License

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

Name		Name	Last commit message	Last commit date
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.github/workflows		.github/workflows
.env		.env
.gitignore		.gitignore
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
app-new.py		app-new.py
app.py		app.py
config.ini		config.ini
requirements.txt		requirements.txt
setup.py		setup.py

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CFO Financial Insight Dashboard — AI-Driven Financial Engineering Agentic System

Overview

🤖 AI Agents Overview - Microsoft Agent Framework with Azure OpenAI

Agent Architecture

Agent Flows

📂 Project Structure

🔧 Prerequisites

🔧 Setup Instructions

1️⃣ Clone Repository

2️⃣ Setup Python Virtual Environment

3️⃣ Install Dependencies

4️⃣ Microsoft Agent Framework Dependency

5️⃣ Install Graphviz (for flow diagrams)

6️⃣ Set Up Environment Variables

📧 Azure Logic App Setup (Email Automation)

1️⃣ Create a New Logic App

2️⃣ Design the Logic App Workflow

Step A: Add HTTP Trigger

Step B: Add Send Email Action

Step C: Add Response Action

3️⃣ Save and Get the Endpoint URL

4️⃣ Complete Logic App Workflow Diagram

5️⃣ Test the Logic App

6️⃣ Troubleshooting Logic App

7️⃣ Security Best Practices

📜 Azure AI Agent Service SDK Setup

1️⃣ Install Azure SDK

2️⃣ Authenticate and Set Up Azure Service

3️⃣ Retrieve API Key

🚀 Running the Project

Dashboard Tabs

What Happens on Refresh

Email Dispatch Flow

✅ Requirements (requirements.txt)

🛠️ Agent Framework Behavior (Structured Output)

🔌 SAP Connectivity Notes

🐛 Troubleshooting

⚙️ Configuration Tips

🔧 Extending the Agents

🔒 Security

📋 Quick Commands (Windows PowerShell)

🏁 Next Steps

📝 Citation

📜 License

🔗 Related Repositories

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