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Agentic AI system development

Are you ready to unlock the full potential of AI?Do you also what to develop intelligent agents that leverage the capabilities of large language models for advanced reasoning, smart decision-making, and dynamic problem-solving?

>> Available in Danish <<

Prerequisites

This course teaches is aimed towards technical practitioners, software engineers, data scientists or DevOps engineers, that want to build agentic AI systems. The curriculum covers important concepts in AI such as prompt engineering or MCP in theory and puts heavy focus on practical hand-on coding experience.

At the end of the course, students should have a solid understanding of agentic AI systems, limitations and strengths and should have the knowledge to build their own agentic system.

Participant profile:

This course is intended for professional developers or data scientists who have knowledge of Python, and experience with Large Language Models such as ChatGPT, it’s also preferred that you have command line experience.

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Course content:

LLMs – Introduction
  • When to use and when not to use Large Language Models
  • Overview of the current AI landscape (frameworks, libraries, models, user interfaces) and terminology.
  • Introduction to Agentic Systems, architectures.
LLMs in programs
  • How to use LLMs programmatically
  • API usage
LangChain and LangGraph
  • Introduction to LangChain, Langgraph
  • LangGraph development model
  • AI development concepts in LangGraph, LangChain.
  • Implementation of applications that utilize LLMs
Agents
  • Tool us for LLMs

  • Develop LLM ReAct agents

  • Connect the agentic application to MCP servers

RAG
  • Overview of RAG concepts
  • Connecting a RAG source to the application
Testing and UI
  • Learn how to implement a UI for your application
  • Overview of testing concepts for LLMs and agentic applications
Do you have any course related questions, please contact