Learning Paths for Technical Professionals

Agentic AI

This starter learning path introduces the foundations and advanced techniques for building autonomous AI agents. Learners will explore key frameworks such as LangChain, LangGraph, AutoGen, CrewAI, and Semantic Kernel SDK, while mastering agentic design patterns, multi-agent systems, memory architectures, RAG, vector databases, and production deployment. The curriculum emphasizes hands-on projects, real-world use cases, and the integration of open-source and cloud-based AI tools.

Skills:

Autonomous AI agent development

Multi-agent system design

Agent memory architectures

Retrieval-augmented generation (RAG)

Vector database integration

Agentic design patterns

AI integration in applications using Semantic Kernerl SDK

Open-source and cloud AI tool proficiency

Prompt engineering

Learning objectives

  1. Develop Autonomous AI Agents: Build, configure, and deploy AI agents using frameworks like LangChain, LangGraph, CrewAI, and Semantic Kernel SDK.
  2. Implement Multi-Agent Systems: Design and orchestrate collaborative agent workflows with AutoGen and advanced conversation patterns for real-world applications.
  3. Integrate Memory and Retrieval: Equip agents with memory and retrieval-augmented generation (RAG) using vector databases, embeddings, and memory management techniques.
  4. Apply Agentic Design Patterns: Utilize reflection, tool use, planning, and collaboration patterns to architect robust, scalable, and adaptive agentic solutions.
  5. Integrate AI-Driven Plugins in business applications using the Semantic Kernel SDK

Target audience:

This path is designed for software engineers, AI developers, data scientists, and technical professionals seeking to build, deploy, and scale autonomous AI agents. It is suitable for those with foundational programming experience who want hands-on exposure to state-of-the-art agentic frameworks and real-world AI solutions.

Make AI your ally with an AI Starter Path