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learn-agentic-ai

Course material for building agents with OpenAI Agents SDK, Dapr and Kubernetes

Website ↗ (opens in a new tab)Repository on GitHub ↗ (opens in a new tab)
Stars
4.4k
License
MIT
Last commit
Oct 2025
Language
Jupyter Notebook

Missed the latest GitHub check; these facts may be older.

Overview

Learning material for the Panaversity agentic AI program, organized around the DACA design pattern: OpenAI Agents SDK for agent logic, MCP and A2A for tools and agent communication, and Dapr on Kubernetes for scale. It lays out three 14-week courses (AI-201, AI-202, AI-301), quiz and hackathon formats, and a written argument for running 10 million concurrent agents.

Who it is for: Developers learning agentic AI and cloud-native agent deployment

Strengths

  • Structured three-course syllabus from Agents SDK basics to Kubernetes, Dapr and A2A
  • Covers cloud-native topics: Dapr workflows, actors, pub/sub, CKAD, serverless containers
  • Includes video playlists for AI-201 and quiz preparation
  • MIT licensed

Weaknesses

  • README is mostly program strategy and course outline, not runnable setup instructions
  • 10-million-agent feasibility is extrapolation; README admits no direct benchmark exists
  • Tied to an onsite certification program; evaluations are described as onsite only
  • Last commit October 2025 and no releases

What it needs

  • Needs Dapr, Kubernetes, OpenAI Agents SDK, Postgres, Redis, RabbitMQ, CockroachDB
  • Models: OpenAI Agents SDK, Dapr Agents, Google ADK

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