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Build Multi Agent Systems with ADK, Streamlit, Autogen and CrewAI

Sessions 4 days
Duration 30 hrs
Level Intermediate
Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.

Build Multi Agent Systems with ADK, Streamlit, Autogen and CrewAI

Course Code: C991

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Dive into the world of Agentic Process Automation by mastering the orchestration of Large Language Models (LLMs) and building advanced AI-driven applications. This course covers the essentials of LLM AI orchestration, from running local LLMs to building and debugging LLM apps, and offers a deep dive into Retrieval-Augmented Generation (RAG) with techniques like text embedding, vector databases, and similarity searches. You'll also learn to create robust RAG systems that enhance AI capabilities.

Further, explore multi-agent AI workflows by implementing the ReAct agent framework, equipping agents with tools and skills, and setting up multi-agent systems using leading platforms like LangGraph, CrewAI, and AutoGen. Ideal for professionals looking to innovate with AI technologies and build complex automation systems.

No funding is available for this course

For WSQ funding, please checkout the details at WSQ - Building Multi-Agent AI Systems

Course Fee

$1,400.00 (GST-exclusive)

$1,526.00 (GST-inclusive)

Mode of Training *

Physical Classroom Training
Synchronised Teaching using ZOOM
Corporate Training at Company Premise

Course Date * Course Date

Course Time * Course Time

9:30am - 6:30pm

No. of Participants

Sponsorship *

Self-Sponsored
Employer-Sponsored

No. of Participants

Name(s) on Certificate *

Additional Message or Emails for Course Notification

Additional Message or Emails for Course Notification

* Required Fields

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

We provide free consultation related to the subject matter after the course.
Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

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

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

Able to operate using computer functions
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: 18-65 years old

Minimum Software/Hardware Requirement

Software:

TBD

Hardware: Window or Mac Laptops

Job Roles

AI Orchestration Specialist
Large Language Model Engineer
AI Application Developer
RAG Engineer
Vector Database Specialist
Data Engineer
Multi-Agent System Developer
ReAct Agent Implementer
Machine Learning Engineer
AI Workflow Designer
AI Project Manager
AI Systems Integrator
Automation Consultant
Data Scientist
Knowledge Engineer
AI Research Scientist
Digital Transformation Leader
AI Solutions Architect
AI Operations Specialist
Product Innovation Manager

Trainers

Solomon Soh is an experienced Data Scientist and AI Trainer with a strong record of teaching and mentoring in Python programming, data analytics, and machine learning. Currently a Data Science Trainer with IBM Singapore, he has coached teams on projects involving natural language processing, computer vision, and chatbots, achieving a 96% learner satisfaction rating for his communication and technical expertise. His career spans roles at Workforce Optimizer, Certis Cisco, Ernst & Young, and IQVIA, where he applied Python-driven analytics to improve operations, optimize staffing, and deliver actionable insights. His academic background includes a double degree in Economics and Psychology from Singapore Management University (Summa Cum Laude, triple major in Analytics), an MBA, and a Master’s in Financial Engineering.

Yap Swee Leong is an ACLP-certified trainer with experience in adult education and IT-focused training. He has delivered courses across digital systems, IT fundamentals, and business technology, equipping learners with the skills to adapt to digital workplaces. With his strong foundation in instructional design and applied learning, he makes technical concepts accessible to diverse groups of learners. In his network security training, Yap focuses on building beginner-level understanding of key security principles, including password policies, secure browsing, and basic risk management. His sessions are designed to provide non-technical learners with confidence in recognizing and addressing everyday cybersecurity threats. By using practical, relatable examples, he ensures participants develop essential habits for safeguarding personal and organizational data.