Mastering Multi-Agent AI Workflows and Agentic Process Automation (APA)
About This Course
This course offers a deep dive into the orchestration of Large Language Model (LLM) AI, covering the fundamentals of running local LLMs, building and debugging LLM applications, and understanding the complexities of AI-driven text processing. You will explore Retrieval-Augmented Generation (RAG) techniques, including text embedding, vector databases, and similarity search, to enhance the efficiency and performance of AI models in various applications.
Additionally, you will learn to implement multi-agent AI workflows using cutting-edge frameworks like LangGraph, CrewAI, and AutoGen. Gain hands-on experience with the ReAct agent framework, equipping AI agents with the necessary tools and skills to perform complex tasks autonomously. By the end of this course, you will be well-equipped to develop, manage, and optimize advanced AI systems to drive innovation and improve business outcomes.
What You'll Learn
LO1: Evaluate Large Language Model (LLM) AI models by identifying their strengths and limitations.
LO2: Analyze Retrieval-augmented generation (RAG) algorithms to improve efficiency .
LO3: Assess the feasibility of implementing multi-agent AI applications.
Topics Covered:
Topic 1. Introduction to Large Language Model (LLM) AI Orchestration
• Overview of LLM AI orchestration
• Running local LLM
• Building an LLM app
• Debugging LLM app
Topic 2 Retrieval-augmented generation (RAG)
• Overview of Retrieval-augmented generation (RAG)
• Text Embedding
• Vector database
• Similarity Search
• Building an RAG
Topic 3. Implementing a Multi-Agent AI Workflow
• Introduction to the ReAct agent framework
• Implementing a ReAct agent
• Equipping agent with tools and skills
• Overview of multi-agent AI frameworks - LangGraph, CrewAI, AutoGen etc
• Setting up and running your first multi agent workflow
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
Target Age Group: 21 to 65 years old