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WSQ - Autonomous AI Agents

Sessions 2 days
Duration 16 hrs
Level Beginner
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.

TSC Title: Artificial Intelligence Application
TSC Code: AER-TEM-3026-1.1

Funding for this course is valid from 11 Apr 2025 to 10 Apr 2027. Register and complete the course within this period to qualify for funding support.

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

LO1: Implement AI applications by integrating data management principles and fundamental AI concepts to address real-world challenges.
LO2: Deploy AI workflows using machine learning techniques and software development methodologies to optimize performance and accuracy.
LO3: Identify and report issues in AI applications by analyzing implementation procedures and evaluating collected data.

Download Course Brochure

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 - Autonomous AI Agents

Course Code: TGS-2025054471

WSQ
SFC
PSEA
SFEC
Absentee Payroll
MCES

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Autonomous AI Agents equips participants with practical skills to design, build, deploy, and manage AI agents capable of performing tasks with minimal human intervention. Learners will explore how autonomous agents use large language models, tools, memory, context, reasoning, and structured workflows to understand goals, plan actions, make decisions, and interact with digital systems.

Through hands-on activities, participants will build agents that retrieve information, process documents, call APIs, automate workflows, generate outputs, and collaborate with other specialized agents. The course covers agent architecture, prompt and context engineering, tool integration, Retrieval-Augmented Generation, persistent memory, multi-agent coordination, task delegation, and human-in-the-loop approvals.

Participants will also learn to evaluate agent performance, monitor actions, manage permissions, control operating costs, and troubleshoot unreliable behaviour. Strong emphasis is placed on responsible deployment through sandboxing, least-privilege access, security guardrails, audit trails, data protection, and defence against prompt injection. By the end of the course, learners will be able to develop secure, reliable, and goal-driven autonomous AI agents for practical business and operational applications.

This course is part of our AI training in Singapore, where you can browse all our AI courses. See more multi AI agents courses.

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

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 Form Submit PSEA Form

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: Building Autonomous AI Agents with Data, Memory and Tools

Topic 2: Deploying and Optimizing Autonomous AI Agent Workflows

Topic 3: Monitoring, Evaluating and Troubleshooting AI Agent Applications

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.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

You will need the following for this course:

A free account on a generative AI assistant of your choice, e.g. ChatGPT, Google Gemini or Microsoft Copilot (the free tier is sufficient)

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).

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.

Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
We email you an invoice showing the course fee after funding.
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.
Click Submit a Claim and search for this course by its course code, TGS-2025054471.
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).
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.

Check your company's SFEC eligibility and balance on EnterpriseSG.
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.
Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
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 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.

Check that this course shows the PSEA tag on its MySkillsFuture page.
Check your PSEA balance on MOE's Student Finance System with Singpass.
Download and complete the PSEA withdrawal form, then submit it to us.
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.
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.

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.
Discuss the decision with the assessor or the assessment manager first.
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.
Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
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.
You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.

Job Roles

AI Engineer
Machine Learning Engineer
Data Scientist
AI Solutions Architect
AI Researcher
Business Intelligence Analyst
AI Ethics Consultant
IT Specialist – AI
AI Project Manager
Automation Engineer
Cloud AI Engineer
Data Engineer
AI Developer
AI Consultant
Predictive Analytics Specialist
Software Engineer – AI
AI Systems Analyst
Robotics Engineer
Cybersecurity AI Specialist
AI Product Manager

Trainers

Dr. Alfred Ang builds agents that run with minimal human intervention, and he has published several of them as working systems rather than demos. He is a full-stack AI developer and Agentic AI solution architect specialising in multi-AI agent system design with n8n, Langflow, Flowise, the OpenAI Agents SDK, the Google Gemini Agent SDK, the Claude Agent SDK, Microsoft AutoGen and CrewAI, building collaborative, goal-driven and autonomous agent architectures for enterprise use cases. He is the founder of Tertiary Courses and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd. His public GitHub portfolio of more than 300 repositories includes hermes-video-pipeline, an automated production pipeline in which a topic request triggers an agent that researches, scripts, renders, verifies and publishes YouTube videos autonomously; n8n-inventorycontrol, an audit and forecasting agent running on a local LLM that reads stock levels, flags low items, computes replenishment and reports by email or a live dashboard; wsq-courseware-generator-claude-streamlit, a Claude Agent SDK platform that generates assessment plans, facilitator guides, learner guides, lesson plans, slides and brochures; and ai-pei, a website with a self-improving chatbot and an auto-blog scheduler. His profile emphasises system architecture, security, observability and performance optimisation, backed by AWS Certified Solutions Architect and AI Practitioner, Microsoft Certified AI Engineer Associate (AI-102) and CompTIA Security+ credentials. He also holds a Certificate in AI Ethics and Governance (Senior Professional) from NTU and a PhD in Electrical Engineering from NUS, and he has co-invented more than 20 US patents.

In this WSQ Autonomous AI Agents course, Dr. Ang teaches learners to design agents that understand a goal, plan actions, use tools and decide when to stop or ask for help. The first topic covers building autonomous AI agents with data, memory and tools: agent architecture, prompt and context engineering, tool and API integration, short- and long-term memory, and Retrieval-Augmented Generation so that the agent works from trusted information. Learners build agents that retrieve information, process documents, call APIs and collaborate with other specialised agents, following patterns he uses in his own video, inventory and courseware pipelines. The second topic, deploying and optimising autonomous AI agent workflows, moves those agents into structured workflows with triggers, schedules, retries and human checkpoints, and looks at how to reduce cost and latency without losing quality. The third topic, monitoring, evaluating and troubleshooting AI agent applications, reflects the observability focus of his architecture work: learners trace an agent's reasoning and tool calls, define evaluation cases, diagnose failures such as loops, hallucinated tool inputs or missing context, and fix them methodically. Because his own pipelines run unattended, he is direct about the safeguards autonomy requires, including permissions, guardrails, logging and clear accountability, themes reinforced by his AI governance certification. The course is hands-on throughout, and each learner deploys and tests a working autonomous agent workflow. As an ACTA-certified trainer and DACE-certified curriculum developer, he aligns the labs with the WSQ assessment. To see his autonomous agent projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in machine learning, data analytics and AI applications. His hands-on work includes developing predictive models and deep learning solutions using Python, R, TensorFlow and Keras, along with RShiny dashboards for presenting results. He brings a professional background in healthcare, finance and customer analytics, and an applied perspective on AI that addresses both the opportunities and the risks of emerging technologies. He holds a Masters in Education from NTU and has eight years of teaching experience in international schools in Singapore and overseas.

In WSQ Autonomous AI Agents, Dwight draws on his machine learning and data background to help learners build agents that work with data, memory and tools. He guides participants through deploying and optimising autonomous agent workflows, then through monitoring, evaluating and troubleshooting AI agent applications. His experience in evaluating model performance supports the course's emphasis on measuring whether agents actually achieve their goals, and his focus on AI risk keeps human oversight in view. Learners leave able to design, deploy and manage autonomous AI agents that perform tasks with minimal intervention while remaining reliable and accountable.

Tan Woei Ming is a data scientist, AI engineer and robotics trainer with over 15 years of experience in artificial intelligence, machine learning and intelligent automation. He has led AI initiatives in predictive analytics, automation and process optimisation across the semiconductor and manufacturing industries, and his background bridges mechanical design, embedded systems and AI. He holds a Master's in Intelligent Systems from the National University of Singapore and a First-Class Honours in Electrical and Electronic Engineering from NTU. A certified adult educator with the WSQ Advanced Certificate in Learning and Performance (ACLP), he has trained learners in robotics programming, AI integration and computer vision applications.

In WSQ - Autonomous AI Agents, Woei Ming equips learners to build AI-driven systems that act with a degree of independence. Participants build autonomous agents that combine data, memory and tools, deploy and optimise agent workflows, and then monitor, evaluate and troubleshoot agent applications. His experience with automation in real production environments shapes his emphasis on reliability, testing and knowing when human oversight is needed. Through hands-on practice, learners gain the skills to design adaptive, data-driven agents and the judgement to keep them dependable once deployed.

Quah Chee Yong is a data science educator and AI specialist with extensive experience delivering machine learning and deep learning training across academia and industry. A certified adult educator with the WSQ ACLP, he has led AI and data analytics programmes for SAP, Temasek Polytechnic and IMDA under the SGUnited Mid-Career Pathways initiative. His technical expertise spans data science, computer vision and natural language processing using Python, TensorFlow and cloud platforms, and he has delivered corporate workshops on text analytics, recommender systems and chatbot development.

In WSQ Autonomous AI Agents, he combines conceptual clarity with hands-on project work to help learners build agents that pursue goals with minimal human intervention. Participants learn to build autonomous AI agents with data, memory and tools, then deploy and optimise agent workflows, and finally monitor, evaluate and troubleshoot AI agent applications. His experience designing and evaluating machine learning systems is valuable here, because dependable agents depend on clear success criteria and careful testing. Learners leave able to design, deploy and manage autonomous agents that plan actions and complete tasks reliably in a workplace setting.

Teh Siew Yee is a data science and digital transformation leader with more than two decades of experience across the technology, finance and aviation sectors. Siew Yee has held senior leadership roles at Standard Chartered Bank and SIA Engineering Company, overseeing AI, analytics and process innovation initiatives. With postgraduate qualifications in Artificial Intelligence from Singapore Management University, Siew Yee brings deep expertise in machine learning, automation and responsible AI deployment. As an ACLP-certified trainer, Siew Yee delivers WSQ courses that integrate case studies and reflective exercises so learners build both knowledge and confidence.

In Autonomous AI Agents, Siew Yee applies this background in automation and responsible AI to agents that complete tasks with minimal human intervention. Learners first build autonomous agents that draw on data, memory and tools, then deploy and optimise agent workflows so they run efficiently and reliably. The final topic covers monitoring, evaluating and troubleshooting AI agent applications once they are live. Siew Yee's sessions place strong emphasis on ethical AI practice and on aligning agents with data governance frameworks, preparing participants to introduce autonomous agents in their organisations in a way that is effective, accountable and well controlled.