Course Details
Course Details
What You'll Learn
LU1: Agentic AI App Development with Agentic AI Frameworks
T1:Introduction to Agentic AI frameworks - CrewAI, Augtogen and OpenAI ADK
T2 Overview of multi-agent workflows
T3: Agentic AI workflow design
T4: Build Agentic AI Applications with Agentic AI frameworks
T5: Development with AI IDE - GitHub Copilot, Cursor, Trae or Windsurf
LU2: Agentic AI and RAG Deployment on Streamlit Cloud
T1:Concepts behind context augmentation techniques, such as knowledge graphs
T2: Approaches to deploy models for inference using cloud platforms
T3: Identify issues in Agentic AI pipeline by analysing and interpreting error logs
T4: Deploy RAG an Agentic AI Apps on Streamlit Cloud or Railway
LU3: Model Alignment and Guardrails
T1:Safeguarding Large Language Models (LLMs) with guardrails for content generation
T2:Model alignment for safety and alignment
T3:Deploy guardrails on Agentic AI applications.
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.
Target Age Group: 18-65 years old
Minimum Software/Hardware Requirement
Software:
You will need the following for this course:
- Python 3 – free
- Visual Studio Code – free
- A free Google Gemini API key – from Google AI Studio
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-2025059028.
- 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
Job Roles
- AI Application Developer
- Machine Learning Engineer
- AI Solutions Architect
- Data Scientist
- AI Product Manager
- Cloud AI Engineer
- AI Research Engineer
- Software Developer (AI)
- Innovation Technologist
- R&D Engineer
- AI Consultant
- LLM Application Developer
- Full Stack AI Developer
- Automation Engineer
- Technical AI Project Manager
- AI QA Engineer
- Data Engineer
- Intelligent Systems Engineer
- AI Integration Specialist
- AI Systems Analyst
Trainers
Trainers
Dr. Alfred Ang does not stop at prototypes; most of his agentic AI work ends with an application running in the cloud. He is a full-stack AI developer and Agentic AI solution architect who builds production-ready AI solutions on AWS, Kubernetes and modern DevOps stacks, with attention to security, observability and performance. He also serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, having founded Tertiary Courses, and is a courseware developer and corporate trainer. He designs multi-agent systems with CrewAI, Microsoft AutoGen, the OpenAI Agents SDK, the Google Gemini Agent SDK, the Claude Agent SDK, n8n, Langflow and Flowise. His public GitHub portfolio includes Streamlit applications built with the Claude Agent SDK for generating WSQ courseware and course proposals, Docker-based platforms deployed through Coolify, and an n8n HR portal whose chatbot is protected by input and output guardrails. He has also published a set of Claude Code skills, including a secret scanner, reflecting how he uses AI coding assistants in day-to-day development. He is a Neo4j Certified Professional, which underpins his work with knowledge graphs, as well as an AWS Certified Solutions Architect, AWS Certified AI Practitioner, Microsoft Certified AI Engineer Associate (AI-102) and CompTIA Security+ certified professional, and he holds a Certificate in AI Ethics and Governance (Senior Professional) from NTU. He earned a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS.
In WSQ - Build and Deploy Agentic AI Apps with CrewAI, Autogen, ADK and Streamlit, Dr. Ang follows the full life cycle from design to a live, safeguarded app. The first learning unit introduces CrewAI, AutoGen and ADK, multi-agent workflow design and building applications with AI IDEs such as GitHub Copilot, Cursor, Trae or Windsurf. The second unit covers context augmentation techniques, including knowledge graphs, approaches to deploying models for inference on cloud platforms, and reading error logs to diagnose problems in an agentic pipeline, before learners deploy RAG and agentic apps on Streamlit Cloud or Railway. The third unit addresses guardrails for content generation, model alignment for safety, and deploying guardrails on live agentic applications. His graph database certification helps him explain when a knowledge graph adds value over plain vector search, and his cloud and security background shapes practical advice on secrets management, logging and keeping costs predictable. He treats error logs as a teaching tool, deliberately walking learners through failures so they can debug confidently on their own. The approach is hands-on throughout, with learners building, breaking and fixing their own applications, and every activity is aligned to the WSQ assessment. Participants leave with a deployed, guardrailed agentic AI app and the skills to ship the next one. To explore his deployed agentic AI and Streamlit projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Tan Woei Ming is a data scientist, AI engineer and robotics educator with over 15 years of experience in artificial intelligence, machine learning and automation. He has led AI initiatives in predictive analytics and process optimisation across the semiconductor and manufacturing industries. A certified adult educator with the Advanced Certificate in Learning and Performance (ACLP), he has trained learners in robotics, control systems and intelligent applications using ROS 2, Python and embedded platforms. 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.
In WSQ - Build and Deploy Agentic AI Apps with CrewAI, Autogen, ADK and Streamlit, Woei Ming brings a hands-on, systems-thinking approach to agent development. Learners compare agentic AI frameworks such as CrewAI, AutoGen and OpenAI ADK, design multi-agent workflows and build applications with AI coding tools. They then deploy RAG and agentic apps on Streamlit Cloud or Railway, diagnose pipeline issues from error logs, and safeguard large language models with alignment techniques and guardrails. Drawing on his automation experience, he helps learners turn theory into deployable multi-agent applications that are tested, monitored and safe to run.
Teh Siew Yee is a data science and digital transformation leader with more than two decades of industry and teaching experience across the technology, finance and aerospace sectors. Siew Yee has led enterprise-wide AI adoption initiatives, built analytics teams from the ground up, and served as Head of Data Engineering and Analytics at SIA Engineering Company and Senior Data Specialist at Standard Chartered Bank. With postgraduate training in Artificial Intelligence from Singapore Management University, Siew Yee's expertise spans machine learning, blockchain and business intelligence, supported by a strong foundation in Python, Tableau and cloud platforms. Siew Yee is also an ACLP-certified trainer.
In this course, Siew Yee draws on experience in responsible AI and data strategy to teach how agentic applications are built and deployed with CrewAI, AutoGen, OpenAI's ADK and Streamlit. Learners compare agentic AI frameworks, design multi-agent workflows and build applications with the help of AI-assisted IDEs such as GitHub Copilot, Cursor, Trae or Windsurf. They then explore context augmentation techniques such as knowledge graphs, interpret error logs to troubleshoot agentic pipelines, and deploy RAG and agentic AI apps to Streamlit Cloud or Railway, before turning to model alignment. Siew Yee ensures participants gain practical skills to design, monitor and deploy agentic applications fit for enterprise use.
Truman Ng is an AI and network engineering professional with a multidisciplinary portfolio spanning machine learning, blockchain, cybersecurity and automation. His teaching covers Python, TensorFlow, Docker, CI/CD pipelines and robotic process automation, reflecting strong competence in both software engineering and IT infrastructure. An engineering graduate of NUS and a PMP-certified project manager, he also holds Huawei HCIE, Cisco CCNP and Certified Bitcoin Professional credentials, and draws on this cross-domain expertise to connect AI development with real-world deployment.
In WSQ Build and Deploy Agentic AI Apps with CrewAI, Autogen, ADK and Streamlit, Truman connects the curriculum to real-world deployment pipelines, from model orchestration to containerised agent systems. Learners are introduced to agentic AI frameworks, design multi-agent workflows and build agentic applications with the help of AI IDEs such as GitHub Copilot, Cursor, Trae or Windsurf. They then explore context augmentation techniques such as knowledge graphs and RAG, deploy their applications for inference on Streamlit Cloud and learn to identify issues in agentic AI pipelines. His emphasis on modular design, security and scalability prepares learners to deploy CrewAI- and Autogen-based applications efficiently on cloud or on-premise environments.
James Lee Kin Nam is a digital design professional and Adobe Certified Expert / Instructor whose strengths lie in user-centric design and front-end web development, including presenting AI agent outputs through intuitive dashboards and interactive web interfaces. He has over 20 years of training experience in creative technologies and web development, with a background spanning content creation, UX/UI design and digital marketing, and teaching engagements at Singapore Polytechnic, ACE Training and NTU. Proficient in HTML5, CSS3, JavaScript and WordPress, he has led courses that integrate design thinking with coding and analytics for digital innovation.
In WSQ Build and Deploy Agentic AI Apps with CrewAI, Autogen, ADK and Streamlit, James focuses on user-centric design and front-end deployment. Learners are introduced to agentic AI frameworks including CrewAI, Autogen and OpenAI ADK, design multi-agent workflows and build applications with AI IDEs such as GitHub Copilot, Cursor, Trae or Windsurf. They explore context augmentation such as knowledge graphs, interpret error logs, deploy agentic AI and RAG apps on Streamlit Cloud or Railway, and add guardrails for safe, aligned behaviour. By combining creative visualisation with technical implementation, James helps participants bridge data science, automation and user experience design.
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