What is Agentic Ai

Agentic_AI is a progressive learning framework designed for building AI-powered cloud agents using the Dapr Agentic Cloud Ascent (DACA) design pattern. It integrates various technologies such as OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Rancher Desktop, and Kubernetes.

Use cases

Use cases include developing health metric analysis tools like the BMI Calculator, creating interactive dashboards for monitoring AI agents, and implementing scalable systems through progressive tutorials.

How to use

To use Agentic_AI, clone the repository, install the required dependencies, and follow the progressive learning path starting from foundational concepts to advanced implementations and projects. Deployment can be done using Azure Container Apps.

Key features

Key features include an AI-powered BMI Calculator Agent, a real-time Streamlit Dashboard for agent monitoring, nine progressive tutorials from basic to advanced concepts, Dapr integration for state management and pub/sub capabilities, and cloud readiness with Docker support.

Where to use

Agentic_AI can be used in various fields such as healthcare for health metrics analysis, cloud-native application development, and any domain requiring intelligent agent deployment and management.

🚀 DACA Framework

Dapr Agentic Cloud Ascent
Empowering Intelligent Cloud-Native Agent Development

A progressive learning framework for building AI-powered cloud agents with Dapr

📂 Repository Structure

daca-framework/
├── 01_step/ # Foundational concepts
├── 02_step/ # Advanced implementations
├── projects/
│ ├── bmi_calculator/ # AI-powered health metric agent
│ └── streamlit_website/ # Interactive agent dashboard
├── project[1-9].ipynb # Progressive learning notebooks
├── LICENSE
└── README.md

🧠 Framework Philosophy

• Agent-First Design: Treat every component as an intelligent agent
• Cloud-Native DNA: Built for Azure Container Apps & Dapr from inception
• Learn by Doing: Progressive notebooks guide from basics to production

🚀 Getting Started

Prerequisites

• Python 3.8+
• Jupyter Notebook
• Docker Desktop
• Azure Account (for deployment)

# Clone repository
git clone https://github.com/your-org/daca-framework.git
cd daca-framework

Install dependencies

pip install -r requirements.txt
Learning Path
Fundamentals: Run 01_step/ notebooks

Advanced Concepts: Explore 02_step/ modules

Projects: Implement projects/ examples

Production: Deploy with Azure Container Apps

🌟 Key Features

BMI Calculator Agent: AI-powered health metric analysis

Streamlit Dashboard: Real-time agent monitoring interface

9 Progressive Tutorials: From “Hello Agent” to auto-scaling systems

Dapr Integration: Out-of-the-box state management & pub/sub

Cloud-Ready: Containerized agents with Docker support

📚 Project Notebooks

Notebook Description
project1.ipynb Agent fundamentals & local deployment
project2.ipynb Dapr service invocation patterns
project3.ipynb Stateful agent operations
project4.ipynb Pub/sub agent communication
project5.ipynb Azure Container Apps deployment
project6.ipynb Multi-agent orchestration
project7.ipynb Streamlit monitoring dashboard
project8.ipynb Auto-scaling agents
project9.ipynb Production deployment pipeline
🛠️ Example Project: BMI Agent
python

From projects/bmi_calculator

from daca.agents import HealthAgent

agent = HealthAgent()
result = agent.calculate_bmi(height=1.75, weight=68)
print(f"Health Insights: {result[‘analysis’]}")

🤝 Contributing

Fork the repository

Create feature branch (git checkout -b feature/amazing-feature)

Commit changes (git commit -m ‘Add amazing feature’)

Push to branch (git push origin feature/amazing-feature)

Open Pull Request

📜 License

Distributed under the MIT License. See LICENSE for more information.

🌐 Connect

“Join the agentic revolution - where every service becomes an intelligent entity!”

This README:

1. Maintains the DACA branding from previous context
2. Organizes the repository structure clearly
3. Provides progressive learning guidance
4. Highlights key projects and features
5. Supports Jupyter notebook workflows
6. Includes practical code examples
7. Follows standard open-source conventions

Would you like me to add specific technical details or modify any section?