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WSQ - Develop Multi-Agent AI Applications with AutoGen

WSQ - Develop Multi-Agent AI Applications with AutoGen

This WSQ Develop Multi-Agent AI Applications with AutoGen course equips learners with hands-on skills to build intelligent AI systems using cutting-edge tools like AutoGen and CrewAI. Participants will learn how to create AI agents using Autogen AgentChat, understand the architecture and capabilities of large language models (LLMs), and critically evaluate their strengths and limitations in real-world applications.

The course also explores Retrieval-Augmented Generation (RAG) techniques, enabling participants to design efficient AI agents with enhanced data handling. Learners will gain practical knowledge of implementing scalable multi-agent workflows, assessing their feasibility and performance in various operational contexts. This course is ideal for professionals seeking to advance their capabilities in applied AI development and automation.

Learning Outcomes

By end of the course, learners should be able to:

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.

Course Brochure

Download WSQ - Building Multi-Agent AI Systems Brochure

Skills Framework

This course follows the guideline of Artificial Intelligence Application AER-TEM-4026-1.1 TSC under ICT Skills Framework

Certification

Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.
OpenCerts from SkillsFuture Singapore - After passing the assessment(s) and achieving at least 75% attendance, participants will receive a OpenCert (aka Statement of Achievement) from SkillsFuture Singapore, certifying that they have achieved the Competency Standard(s) in the above Skills Framework.

WSQ Funding

WSQ funding is only applicable to Singaporeans and PR. Subject to eligibility, the funding support is subjected to funding caps.

Effective for courses starting from 1 Jan 2024
Full Fee	GST	Nett Fee after Funding (Incl. GST)
		Baseline	MCES / SME
$900.00	$81.00	$531.00	$351.00

Baseline: Singaporean/PR age 21 and above
MCES(Mid-Career Enhanced Subsidy): S'porean age 40 & above

Upon registration, we will advise further on how to tap on the WSQ Training Subsidy.

You can pay the nett fee (after the WSQ training subsidy) by the following :

SkillsFuture Enterprise Credit (SFEC)

Eligible Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.Click here to submit SkillsFuture Enterprise Credit

SkillsFuture Credit (SFC)

Eligible Singapore Citizens can use their SFC to offset course fee payable after funding but the $4,000 Additional SFC (Mid-Career Support) cannot be used. Click here for SkillsFuture Credit submission

UTAP

Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped up to $250/year and for members aged 40 and above, capped up to $500/year. Click here to submit UTAP

PSEA

Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding.

To check for Post-Secondary Education Account (PSEA) eligibility for this course, Visit SkillsFuture (course code: TGS-2024045806)

Scroll down to “Keyword Tags” to verify for PSEA eligibility.
If there is “PSEA” under keyword tags, the course is eligible for PSEA.

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form and email to us.

Course Code: TGS-2024045806

Post-Course Support

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.

Course Cancellation/Reschedule Policy

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commerce.
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 AutoGen and CrewAI

Introduction to LLM models and AI agents
Create an AI agent with Autogen AgentChat

Topic 2: Retrieval-Augmented Generation (RAG)

Overview of Retrieval-augmented generation (RAG)
Building a RAG Agent

Topic 3: Implementing a Multi-Agent AI Workflow

Introduction to Multi Agents
Create Multi Agent Workflows with Autogen

Final Assessment

Written Assessment - Short Answer Questions (WA-SAQ)
Practical Performance (PP)

Course Info

Promotion Code

Promo or discount cannot be applied to WSQ courses

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.

Minimum Software/Hardware Requirement

Softtware: Windows / Mac

Hardware: Laptop

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity.

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Individual Sponsored Trainee

Employer Sponsored Trainee

Singapore Citizens or Singapore Permanent Residents aged 21 and above
From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
Trainee must pass all prescribed tests / assessments and attain 100% competency.
We reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria.

Singapore Citizens or Singapore Permanent Residents who are DIRECT EMPLOYEE of the sponsoring company.
From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
Trainee must pass all prescribed tests / assessments and attain 100% competency.
We reserves the right to claw back the funded amount from the employer if trainee did not meet the eligibility criteria.

SkillsFuture Credit:

Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.

PSEA:

To check for Post-Secondary Education Account (PSEA) eligibility, goto mySkillsFuture portal and search for this course code.
Scroll down to "Keyword Tags" to verify for PSEA eligibility.
If there is “PSEA” under keyword tags, the course is eligible for PSEA.
And if there is no “PSEA” under keyword tags, the course is ineligible for PSEA.
Not all courses are eligible for PSEA funding.

Absentee Payroll (AP) Funding:

$4.50 per hour, capped at $100,000 per enterprise per calendar year.
AP funding will be computed based on the actual number of training hours attended by the trainee.

SFEC:

If the Training Provider has submitted an enrolment for course fee grant claim in Training Partners Gateway (TPGateway), SSG would be able to derive SFEC funding based on this record. There is no need for enterprise to submit any claim request and the SFEC claim will be automatically generated and disbursed.
Where there is no such record, eligible employers are required to submit an SFEC claim after course completion via the SFEC microsite.
SkillsFuture Enterprise Credit (SFEC) Microsite

Appeal Process

The candidate has the right to disagree with the assessment decision made by the assessor.
When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
The candidate must lodge the appeal within 7 days, giving reasons for appeal
The assessor can help the candidate with writing and lodging the appeal.
he assessment manager will collect information from the candidate & assessor and give a final decision.
A record of the appeal and any subsequent actions and findings will be made.
An Assessment Appeal Panel will be formed to review and give a decision.
The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
Please click the link below to fill up the Candidates Appeal Form.

Job Roles

AI Systems Architect
Multi-Agent Systems Developer
LLM AI Specialist
RAG Algorithm Engineer
AI Orchestration Consultant
Machine Learning Engineer
Data Scientist
AI Application Developer
AI Research Scientist
Software Developer for AI Systems
AI Strategy Consultant
Technology Innovation Manager
AI Project Manager
Natural Language Processing Engineer
AI Solutions Architect
Cloud Computing Specialist
AI Workflow Analyst
Embedded Systems Engineer
Robotics Engineer
Digital Transformation Advisor

Trainers

Tan Woei Ming: Tan Woei Ming is a data scientist and AI specialist with deep expertise in machine learning, deep learning, and semiconductor process automation. He holds a Master’s degree in Intelligent Systems from the National University of Singapore and a First-Class Honours degree in Electrical and Electronic Engineering from Nanyang Technological University. With over 15 years of industry experience at Micron Semiconductor Asia, he led cross-functional projects involving wafer map pattern recognition, robotic fault detection, and cloud-based yield analytics using TensorFlow and Apache Spark. His work has contributed significantly to improving manufacturing efficiency and diagnostic automation.

As an ACLP-certified trainer, Woei Ming combines technical rigor with engaging, application-driven teaching. In “Develop Multi-Agent AI Applications with AutoGen,” he draws from his real-world expertise in computer vision, AI modeling, and automation to guide learners through designing intelligent agent architectures. His sessions emphasize hands-on experimentation—bridging theory, coding, and system integration to help professionals deploy collaborative AI agents for real-world problem-solving and industrial automation.

Yeo Hwee Theng: Yeo Hwee Theng is a data science leader and AI strategist with over a decade of experience in delivering enterprise AI solutions across healthcare, fintech, and government sectors. She currently serves as the Data & Analytics Product Lead at Amplify Health, where she designs and executes AI-driven transformation strategies. Her prior roles include AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot, where she led multi-market AI deployments and managed Asia-based data teams. She holds a Master of Technology in Enterprise Business Analytics from NUS and the Advanced Certificate in Learning and Performance (ACLP) from IAL.

In “Develop Multi-Agent AI Applications with AutoGen,” Hwee Theng helps learners explore the intersection of automation, AI ethics, and real-world deployment. Her sessions emphasize the use of multi-agent frameworks to accelerate intelligent decision-making across sectors such as healthcare, finance, and manufacturing. Through practical projects and guided experimentation, she equips participants to conceptualize, design, and orchestrate autonomous agents that collaborate effectively in complex digital ecosystems.

Teh Siew Yee: Teh Siew Yee is a veteran analytics leader, data scientist, and educator with over 25 years of experience in data-driven transformation across global enterprises. He has held leadership positions at organizations such as TikTok, Standard Chartered Bank, Hewlett-Packard, and SIA Engineering, where he spearheaded large-scale data analytics, AI governance, and automation initiatives. Siew Yee holds a Master of IT in Business (Artificial Intelligence) from SMU, a Bachelor of Electrical and Electronics Engineering from NTU, and is ACLP 2.0 certified. His expertise spans NLP, machine learning, blockchain, and knowledge graph systems.

As an experienced lecturer at PSB Academy and Emarsity, Siew Yee is recognized for his ability to make complex technical concepts accessible and industry-relevant. In “Develop Multi-Agent AI Applications with AutoGen,” he leverages his rich background in responsible AI and enterprise automation to teach learners how to design scalable, transparent, and ethical agent-based systems. His approach blends academic rigor with real-world case studies, enabling professionals to apply AutoGen frameworks to build intelligent, collaborative AI systems for digital transformation.

Truman Ng: Truman Ng is a senior IT and cloud computing trainer with over two decades of experience in enterprise networking, cybersecurity, and cloud automation. Certified in PMP®, ACTA, and Huawei HCIE®, he has trained and mentored IT professionals in DevOps, Microsoft Azure, and AI integration across global corporations. His expertise lies in bridging AI, cloud infrastructure, and system automation, helping organizations achieve operational agility and technological resilience.

In “Develop Multi-Agent AI Applications with AutoGen,” Truman introduces participants to real-world agentic workflows that combine cloud scalability with AI reasoning. His training emphasizes the deployment of multi-agent systems in DevOps, automation, and data analytics contexts—empowering learners to build AI-driven architectures that enhance collaboration, autonomy, and system intelligence within enterprise environments.

James Lee Kin Nam: James Lee is a seasoned digital media and IT trainer with over 20 years of experience in creative technology, multimedia production, and IT education. An Adobe Certified Expert and ACLP-qualified instructor, James has taught professionals and educators in fields such as digital design, cloud-based collaboration, and AI-driven content generation. He has conducted programs for universities and corporate clients, integrating creative and technical skill sets to prepare learners for the digital economy.

In “Develop Multi-Agent AI Applications with AutoGen,” James helps participants explore how AI agents can augment digital design and content automation workflows. His sessions focus on creative applications of multi-agent collaboration, such as automated ideation, data-driven storytelling, and AI-assisted visualization. By blending his experience in design and technology, he inspires learners to use AutoGen not only as a technical framework but also as a tool for innovation across creative and computational domains.

Customer Reviews (20)

Average Rating: 4.7/5 Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

N/A (Posted on 3/13/2026)

Average Rating: 5.0/5 Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

N/A (Posted on 3/13/2026)

Average Rating: 4.7/5 Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

Classroom is cramped. But other then that, I had a good learning experience (Posted on 3/13/2026)

Average Rating: 5.0/5 Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

Great content (Posted on 3/13/2026)

Average Rating: 5.0/5 Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

N/A (Posted on 3/13/2026)

Average Rating: 4.3/5 Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

N/A (Posted on 3/13/2026)

Average Rating: 3.7/5 Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

N/A (Posted on 3/13/2026)

Average Rating: 5.0/5 Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

A very well trained and experienced trainer that shared lots of useful knowledge and real-life examples to the trainees. (Posted on 3/13/2026)