Course Information

  • Sessions 2 days
  • Duration 16 hrs
  • Level Intermediate
  • Assessment 2 hrs

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.

Skills Framework

TSC Title
Artificial Intelligence Application
TSC Code
AER-TEM-4026-1.1

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

Certification

  • 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.
  • 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 - Develop Multi-Agent AI Applications with AutoGen

Course Code: TGS-2024045806
  • WSQ
  • SFC
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES
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What's This Course About

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.

WSQ Funding

Full Fee $900.00 Before GST
GST $81.00 9% of fee
Baseline Nett $531.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $351.00 SG age 40+ · 70% funded
SkillsFuture Enterprise Credit (SFEC)

Eligible companies can use their $10,000 SFEC to cover up to 90% of out-of-pocket course fees for classes ending on or before 30 Nov 2026. A redesigned SFEC starts on 1 Dec 2026.

View on SkillsFuture for Business

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.

Direct Application on SkillsFuture Portal

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.

Submit UTAP Claim

PSEA

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

Check PSEA Eligibility

  • 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, then submit the completed form to us:

PSEA Withdrawal FormSubmit PSEA Form

Course FeeBefore Funding

$900.00 (GST-exclusive)
$981.00 (GST-inclusive)

Course Date

* Required Fields

Additional Note

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

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.

Cancellation & Reschedule Policy

  • 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

Course Details

What You'll Learn

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

Assessment

  • Written Exam
  • Practical Exam

Course Info

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

Software:

You will need the following for this course:

Hardware: Windows PC or Mac laptop

Funding Eligibility Criteria

Self-sponsored learners (individuals)

  • Singapore Citizens and Singapore Permanent Residents aged 21 and above.
  • Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
  • Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.

Company-sponsored learners

  • Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
  • Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
  • Employers can also claim Absentee Payroll funding for employees who attend during working hours.
  • Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).

Conditions for all funded learners

  • Attendance is taken digitally with Singpass for every session, including the assessment.
  • You must complete the course and pass all assessments (assessed as Competent).
  • If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
  • 9% GST is charged on the full course fee before funding.
  • Promo and discount codes cannot be used on funded courses.
  • Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.

How to Apply SkillsFuture Credit Claim for Individuals

Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.

  1. Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
  2. We email you an invoice showing the course fee after funding.
  3. From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
  4. Click Submit a Claim and search for this course by its course code, TGS-2024045806.
  5. Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
  6. Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.

Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.

How to Apply SkillsFuture Enterprise Credit (SFEC)

SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.

Current SFEC: classes ending on or before 30 November 2026

  • Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
  • SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.
  1. Check your company's SFEC eligibility and balance on EnterpriseSG.
  2. Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
  3. Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
  4. No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.

Any unused balance of the current SFEC expires on 30 November 2026.

Redesigned SFEC from 1 December 2026

Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.

How to Apply UTAP (NTUC Members)

NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.

Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.

  1. Make sure your NTUC membership is paid up before the course starts.
  2. Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
  3. Attend at least 75% of the course and sit all assessments.
  4. Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
  5. Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
  6. NTUC usually pays the claim within 4 weeks of submission.

Read more in our UTAP guide.

How to Apply PSEA

Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.

  1. Check that this course shows the PSEA tag on its MySkillsFuture page.
  2. Check your PSEA balance on MOE's Student Finance System with Singpass.
  3. Download and complete the PSEA withdrawal form, then submit it to us.
  4. We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
  5. MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.

More details: MOE PSEA overview.

Appeal Process

If you disagree with an assessment decision, you have the right to appeal.

  1. When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
  2. Discuss the decision with the assessor or the assessment manager first.
  3. If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
  4. Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
  5. An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
  6. You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.

Job Roles

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

Trainers

Dr. Alfred Ang works with Microsoft AutoGen and CrewAI as part of his everyday engineering, designing multi-agent systems that automate customer service, internal workflows, analytics and decision support. He is a full-stack AI developer, Agentic AI solution architect, courseware developer and corporate trainer, and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, having founded Tertiary Courses. Retrieval-augmented generation is a recurring theme in his public GitHub portfolio. His n8n RAG demo pairs a browser PDF uploader with an in-memory vector store so that a Telegram AI agent powered by Google Gemini can answer questions from users' documents, and a second demo ingests 20 mock course brochures into Supabase, Pinecone or Qdrant vector databases to power a customer-service chatbot. His digital human service trains a voice-enabled avatar on an organisation's own content using RAG. He is a Neo4j Certified Professional, bringing graph database knowledge to knowledge-grounded retrieval, as well as a Microsoft Certified AI Engineer Associate (AI-102) and Data Scientist (DP-100). He also holds AWS Certified Solutions Architect and AI Practitioner certifications and a Certificate in AI Ethics and Governance (Senior Professional) from NTU. His academic foundation includes a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU, a B.Sc (Hons) in Physics from NUS and a Graduate Diploma in Intelligent Robotics Systems from NUS-ISS, and he has published more than 20 peer-reviewed papers.

In WSQ - Develop Multi-Agent AI Applications with AutoGen, Dr. Ang takes learners from a single agent to a coordinated team. The first topic introduces AutoGen and CrewAI, the architecture and capabilities of large language models, and how AI agents differ from simple chatbots, before learners create their first agent with AutoGen AgentChat. The second topic covers retrieval-augmented generation: chunking and embedding documents, retrieving relevant passages and building a RAG agent that answers from an organisation's own knowledge rather than guessing. The third topic implements multi-agent workflows, where learners define agent roles, conversation patterns and termination conditions so that agents plan, critique and refine work together. His experience building RAG chatbots on several vector databases allows him to explain the practical trade-offs, such as chunk size, retrieval quality and cost, and to show how to check whether an agent's answers are actually grounded. He also discusses how to critically evaluate LLM outputs, keep humans in control of important decisions and apply responsible AI principles. The class is code-driven: he builds each example live, then supports learners as they extend it to their own use cases, and every activity is aligned to the WSQ assessment. Participants leave with working AutoGen agents, a RAG pipeline and a multi-agent workflow they can adapt at work. To explore his RAG and agentic AI projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Yeo Hwee Theng is a data science leader and AI strategist whose work centres on enterprise AI adoption, machine learning and data architecture across the healthcare, fintech and government sectors. As Data & Analytics Product Lead at Amplify Health, she leads large-scale data transformation and machine learning projects aimed at better business outcomes. Before that she was an AI & Data Architect at Huawei International and a 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 the National University of Singapore and an Advanced Certificate in Learning and Performance (ACLP).

In Develop Multi-Agent AI Applications with AutoGen, she draws on this enterprise deployment experience to show learners how large language models and AI agents fit into real business settings. Learners start with the foundations of LLMs and AI agents, build their first agent with AutoGen AgentChat and compare it with CrewAI, then design a Retrieval-Augmented Generation (RAG) agent that answers questions from their own data. The course closes with multi-agent workflows in which several agents collaborate on a task. Her practical, guided approach helps participants judge where agents add value, weigh their strengths and limitations, and leave able to plan and build multi-agent solutions for their own organisations.

Tan Woei Ming is a data scientist and AI engineer with over 15 years of industry experience in machine learning, deep learning and intelligent automation. 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, work that improved manufacturing efficiency and diagnostic automation. He holds a Master's in Intelligent Systems from the National University of Singapore and a First-Class Honours degree in Electrical and Electronic Engineering from Nanyang Technological University. An ACLP-certified trainer, he combines technical rigour with engaging, application-driven teaching.

In WSQ - Develop Multi-Agent AI Applications with AutoGen, Woei Ming draws on his experience of building and integrating AI systems to guide learners through agent architectures. Participants are introduced to large language models, AI agents, AutoGen and CrewAI, create an agent with AutoGen AgentChat, build a retrieval-augmented generation (RAG) agent, and then implement multi-agent workflows in which agents collaborate on a task. His sessions emphasise hands-on experimentation that links theory, coding and system integration. Learners leave able to design, build and test collaborative AI agents for practical business and automation problems.

Teh Siew Yee is an analytics leader, data scientist and educator with more than two decades of experience in data-driven transformation at global enterprises. Siew Yee has held leadership positions at TikTok, Standard Chartered Bank, Hewlett-Packard and SIA Engineering, spearheading large-scale data analytics, AI governance and automation initiatives. Siew Yee holds a Master of IT in Business (Artificial Intelligence) from Singapore Management University and a Bachelor of Electrical and Electronic Engineering from Nanyang Technological University, is ACLP 2.0 certified, and has expertise spanning natural language processing, machine learning, blockchain and knowledge graph systems. Siew Yee has also lectured at PSB Academy and Emarsity.

In this course, Siew Yee applies that background in NLP, responsible AI and enterprise automation to the design of collaborative agent systems. Learners are introduced to large language models, AI agents and the AutoGen and CrewAI frameworks, and create their first agent with AutoGen AgentChat. They then explore retrieval-augmented generation and build a RAG agent that grounds its answers in source documents, before combining several agents into a multi-agent workflow with AutoGen. Blending academic rigour with real-world case studies, Siew Yee helps professionals design agent-based systems that are transparent, scalable and suited to digital transformation work in their own organisations.

Truman Ng focuses on bringing AI into real enterprise systems, bridging AI, cloud infrastructure and system automation so that organisations gain both agility and resilience. A senior IT and cloud computing trainer with over two decades of experience in enterprise networking, cybersecurity and cloud automation, he has trained and mentored IT professionals at global corporations in DevOps, Microsoft Azure and AI integration. He holds PMP and Huawei HCIE certifications and is an ACTA-certified trainer, bringing a structured, practitioner's approach to every class.

In WSQ Develop Multi-Agent AI Applications with AutoGen, Truman introduces participants to real-world agentic workflows that combine cloud scalability with AI reasoning. Learners start with large language models and AI agents, building a first agent with AutoGen AgentChat and comparing it with CrewAI, then add Retrieval-Augmented Generation so agents can draw on their own knowledge sources. The course culminates in multi-agent workflows built with AutoGen, where Truman shows how such systems apply in DevOps, automation and data analytics contexts. Participants leave able to build AI-driven architectures that improve collaboration, autonomy and intelligence within enterprise environments.

Review

Customer Reviews (20)

Average Rating: 4.7/5 Review by Course Participant/Trainee
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Classroom is cramped. But other then that, I had a good learning experience (Posted on 3/13/2026)
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Great content (Posted on 3/13/2026)
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