patchy631/ai-engineering-hub
AI Engineering Hub offers in-depth tutorials and practical resources on Large Language Models, Retrieval-Augmented Generation, and real-world AI agent applications for all skill levels.
Awesome MCP › MCP Resources & Educational Materials
panaversity/learn-agentic-ai
The project "Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern" is an educational and practical initiative aimed at training developers in building scalable, resilient, and efficient agentic AI systems. It is part of the Panaversity Certified Agentic & Robotic AI Engineer program and covers advanced AI courses AI-201, AI-202, and AI-301. The core focus is on leveraging the Dapr framework, OpenAI Agents SDK, Model Context Protocol (MCP), Agent2Agent (A2A) communication, knowledge graphs, and Kubernetes orchestration to create multi-agent AI systems capable of handling up to 10 million concurrent AI agents. The project addresses the critical challenge of designing AI agents that can operate at massive scale without failure, especially under constrained financial resources typical in educational settings. It presents a detailed argument supported by real-world benchmarks and case studies demonstrating Kubernetes' ability to scale to thousands of nodes and pods, Dapr's actor model efficiency in managing millions of virtual actors with low latency, and the feasibility of handling large-scale AI workloads using GPU clusters orchestrated by Kubernetes. The Dapr Agentic Cloud Ascent (DACA) design pattern integrates OpenAI Agents SDK for agent logic, MCP for standardized tool usage, and A2A for inter-agent communication, all deployed on cloud-native platforms like Kubernetes or Azure Container Apps. This pattern emphasizes AI-first and cloud-first principles, promoting stateless, containerized applications that can scale from local development environments to planetary-scale production systems. The project also explores practical strategies for students and developers to simulate large-scale agentic AI systems using open-source tools, cloud credits, and optimized resource management to overcome budget limitations. It highlights the importance of resilience, monitoring, and efficient networking to maintain high availability and performance. Overall, this repository serves as a comprehensive learning and development platform for agentic AI, focusing on the Model Context Protocol (MCP) as a key component in building interoperable and scalable AI agent systems.
https://github.com/panaversity/learn-agentic-ai
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