The list of 8 agents working togther, as 3 teams

• Research agent
• Product collabration agent
• Marketing Plan agent

colabgen-report-links: https://drive.google.com/drive/folders/1iFzSJ8_4jZUz22yva-jlL2CUQL9bq0KK?usp=sharing

video demo - https://streamyard.com/23za4m5wbj8d

Deployed UI- https://purple-sea-012e84c1e-preview.westus2.6.azurestaticapps.net/

Deployed BackendUrl - https://ms-product-backend.politedesert-4d05d312.eastus2.azurecontainerapps.io/docs

in the end it will give you beautiful report with properly fromatted data and action plan

Multi-AI Agent Collaboration System – Hackathon Submission This project showcases a coordinated system of autonomous AI agents working together to simulate real-world cross-functional collaboration between companies and internal teams. Each agent operates with a clear responsibility—ranging from industry-specific research to innovation planning, market strategy, and executive reporting.

The system demonstrates how distributed intelligence can streamline complex workflows and accelerate decision-making in enterprise environments.

Agent Overview: Agent 1 & Agent 2: Representing Company 1 and Company 2 respectively, for example, Microsoft as Company 1 and Samsung as Company 2 and vice versa. These agents gather company-specific insights, product developments, and competitive intelligence.

Agent 3: Focuses on identifying innovative opportunities and extracting unique selling propositions (USPs) from the combined research findings.

Agent 4: Acts as the review and summarization unit, distilling relevant insights and storing them for future reference and reuse.

Agent 5 & Agent 6: Conduct market research and analyze sales data across regions, then formulate high-performing country-specific marketing strategies.

Agent 7: Compiles dashboard-level summaries for senior management and integrates with customer support systems for real-time interaction.

Instruction for Running the backend

FastAPI Dockerized App

This is a Dockerized FastAPI backend application. Follow the instructions below to build and run it locally using Docker.

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🐳 Running the FastAPI App with Docker (Local Development)

✅ Prerequisites

• Docker Desktop
• Git
• (Optional) Docker Buildx (already included in Docker Desktop for macOS/Windows)

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📦 Step 1: Clone the Repository

git clone https://github.com/yourusername/your-fastapi-app.git
cd your-fastapi-app

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🛠 Step 2: Build the Docker Image

Option A: On Apple Silicon (M1/M2/M3 Macs)

To ensure compatibility with cloud platforms like Azure (which use amd64):

docker buildx create --use  # Only once if not already done
docker buildx build --platform linux/amd64 -t fastapi-local --load .

Option B: On Intel-based Macs or Linux/Windows

docker build -t fastapi-local .

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🚀 Step 3: Run the Container

Run with default port (8003):

docker run -p 8003:8003 fastapi-local

Or bind to another local port (e.g., 8080):

docker run -p 8080:8003 fastapi-local

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🌐 Step 4: Access the API

Once the container is running, open your browser:

• Default route: http://localhost:8003
• API docs (Swagger UI): http://localhost:8003/docs
• you can use frontend url for ease acess : Deployed UI

If using a different port, adjust URLs accordingly.

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🧪 Optional: Run Without Docker (for local debugging)

pip install -r requirements.txt

uvicorn main:app --host 0.0.0.0 --port 8003 --reload

or

python main.py

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🛠 Troubleshooting

Issue	Solution
localhost refused to connect	Make sure the container is running with docker ps.
Port already in use	try to use :8003 as frontend can read that on your local (-p 8080:8003)
No response from app	Check logs with docker logs <container-id>
Docker DNS issues	Restart Docker Desktop or try docker system prune

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📁 Project Structure (Example)

Backend/
├── main.py
├── requirements.txt
├── Dockerfile
└── README.md

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