Learn Agentic AI with DACA Pattern

The learn-agentic-ai repository by Panaversity leverages the Dapr Agentic Cloud Ascent (DACA) design pattern alongside the OpenAI Agents SDK, memory management, MCP, A2A protocols, and knowledge graphs to guide developers from basics to planetary-scale Agentic AI deployments.

It includes hands-on pathways covering Kubernetes, Dapr, Rancher Desktop, and cloud-native technologies for seamless local-to-production transitions.
This project equips you with the core concepts and practical strategies needed to power highly concurrent intelligent agents in your applications.

Usage

1. Navigate to the project repository: learn-agentic-ai on GitHub

2. Clone the repo: git clone https://github.com/panaversity/learn-agentic-ai.git

3. Install dependencies: from the root directory run npm install or pip install -r requirements.txt (see README for details).

4. Configure Dapr components and Kubernetes cluster following the DACA guide, then launch example services.

5. Refer to official docs for deeper insights:

   • Actors Overview · Dapr Docs

   • Agents SDK Guide · OpenAI Platform

Features

• DACA Design Pattern: A cloud-first, AI-first framework enabling standardized development from local prototypes to planetary-scale deployments.

• OpenAI Agents SDK Integration: Native support for Agents, Handoffs, and Guardrails to streamline multi-agent orchestration.

• MCP & A2A Protocols: Facilitate standardized tool contexts and efficient inter-agent communication.

• Kubernetes & Dapr Support: Utilize Dapr’s actor model to schedule thousands of virtual agents at millisecond latency on a single core.

• Knowledge Graph & State Stores: Combine graph databases with Dapr state management to achieve high throughput and low-latency state interactions.

Use Cases

• Large-Scale Distributed AI: Cloud-native applications requiring support for millions of concurrent agent interactions.

• Intelligent Automation: Automate complex multi-agent workflows for customer support and DevOps tasks.

• IoT & Edge Computing: Deploy lightweight multi-agent architectures on resource-constrained hardware.

• Education & Research: Rapidly prototype and experiment with agentic AI systems for academic projects.