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learn agentic ai

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Multi-Agent Platforms & Frameworks
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Basic Information

This repository provides structured learning material, curriculum, and design guidance for building agentic AI systems using the Dapr Agentic Cloud Ascent (DACA) design pattern. It is part of the Panaversity Certified Agentic & Robotic AI Engineer program and targets Agentic AI developers and AgentOps professionals. The README collates an executive summary of DACA, a feasibility argument for scaling agentic systems to millions of agents using Kubernetes and Dapr, comparisons of agent frameworks with emphasis on the OpenAI Agents SDK, architecture diagrams, a comprehensive DACA guide, course outlines, quiz and evaluation plans, and resources such as video playlists and practice prompts. The repo is intended as both a teaching syllabus and a reference for cloud-first, AI-first multi-agent system design that integrates Dapr capabilities, Model Context Protocol (MCP), Agent2Agent (A2A) communication, and OpenAI Agents SDK primitives.

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App URL
https://github.com/panaversity/learn-agentic-ai
Github Repository
https://github.com/panaversity/learn-agentic-ai/blob/main/README.md

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App Details

Features
The repository documents the Dapr Agentic Cloud Ascent (DACA) design pattern and highlights its core components: Dapr for distributed state, actors, pub/sub and workflows; OpenAI Agents SDK for agent logic and orchestration; Model Context Protocol (MCP) for standardized tool usage; and the Agent2Agent (A2A) protocol for inter-agent communication. It includes a comparative table of agent frameworks, architecture diagrams and images, a comprehensive guide to DACA, course curricula (AI-201, AI-202, AI-301) covering FastAPI, containerization, Kubernetes, Dapr features, memory systems and self-hosted LLMs, quizzes and hackathons for evaluation, recommendations for scaling and cost optimization, and preparation material for CKAD and Dapr-specific topics.
Use Cases
This repo helps developers and architects learn how to design, build, and scale cloud-native multi-agent AI systems using Dapr and the OpenAI Agents SDK. It offers a stepwise curriculum with hands-on topics such as containerization, Kubernetes, Dapr state/workflows/actors, FastAPI, Postgres/Redis, messaging, self-hosted LLMs, and tuning for scale. The materials include exam-style quizzes, hackathon prompts, an executive DACA summary, practical guidance for simulating large-scale loads on limited budgets, and recommended evaluation paths like CKAD and agent-native deployment simulations. Overall it prepares learners for production patterns, resiliency and scalability considerations, and operational practices for agentic systems.

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