WSQ - Agentic AI Applications with Claude Code

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In today’s rapidly advancing AI landscape, the ability to build agentic AI applications is becoming a critical capability for developing intelligent, autonomous, and context-aware systems. This course, Building Agentic AI Applications with Claude Code, equips learners with the technical skills to design, develop, and deploy AI-powered applications using modern large language models and agent-based architectures.

Learners will be introduced to Claude Code as a development approach for creating AI-driven solutions through structured prompting, code generation, and iterative workflows. The course covers the fundamentals of agentic AI, including how to design systems where AI agents can reason, plan, and execute tasks across multi-step workflows.

Participants will gain hands-on experience in building AI pipelines using frameworks such as LangChain, and implementing Retrieval-Augmented Generation (RAG) to enable applications to access and utilize external knowledge sources effectively. The course also explores how to develop AI agents and multi-agent systems that can interact with tools, APIs, and knowledge bases to deliver dynamic and context-aware responses.

In addition to development, the course emphasizes practical considerations such as debugging, optimization, and reliability of RAG and agent-based systems, enabling learners to identify and resolve common challenges in real-world implementations.

By the end of the course, learners will be able to design and build scalable, intelligent, and autonomous AI applications, leveraging Claude Code methodologies and agentic AI patterns to create impactful solutions across various domains.

This course is suitable for developers, AI practitioners, and technology professionals seeking to develop hands-on expertise in agentic AI, RAG systems, and modern AI application development.

Learning Outcomes

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

LO1: Implement AI applications using AWS Bedrock and Foundation Models
LO2: Deploy AI workflows leveraging LangChain and machine learning techniques.
LO3: Identify and report issues in Retrieval-Augmented Generation (RAG) and AI Agents applications.

Course Brochure

Download WSQ - Agentic AI Applications with Claude Code Brochure

Skills Framework

This course follows the guideline of Artificial Intelligence Application EGS-TEM-3026-1.1 under Engineering Services 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.
External Certification - The participant will be able to attempt the AWS Certified Machine Learning Engineer Associate exam after attending this course. Upon passing the exam, you will receive the AWS Certified Machine Learning Engineer Associate accreditation.

AWS Skill Builder

We are authorised AWS reseller of AWS Skill Builder. If you would like to subscribe to AWS Skill Builder, please register you interest here.

Certification Exam at Pearson Vue

Once you are prepared for the exam, you can register for the CLF-C02 AWS Certified Cloud Practitioner Exam here. We are Authorised Pearson Vue Testing Center. You can take the certification exam at our test center. Note that the course fee does not include the certification exam fee.

You can purchase the exam voucher (one of the lowest prices in Singapore) at AWS Foundational Certification Exam Voucher

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
$500.00	$45.00	$295.00	$195.00

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-2025052468)

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-2025052468

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: Claude Code Fundamentals

Vibe Coding with Claude Code
Initialise Claude Code
Operation Modes
Context Engineering

Topic 2: Tools and Commands

Memory Management
Tools & MCP
Slash Commands

Topic 3: RAG, SKills and Agents

Skills
Sub Agents
Claude Github Actions

Final Assessment

Written Assessment (SAQ)
Practical Performance

Course Info

Minimum Entry Requirement

Knowledge and Skills

Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
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 18 years old

Minimum Software/Hardware Requirement

Software:

You can download and install the following software:

Magneto

Hardware: Windows and Mac Laptops

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

Job Roles

AI Developer
Machine Learning Engineer
Generative AI Specialist
Cloud Engineer
Data Scientist
AI Solution Architect
AI Workflow Developer
RAG Application Specialist
AI Model Trainer
LangChain Developer
Software Engineer (AI Systems)
AI Project Manager
Knowledge Base Engineer
Foundation Model Specialist
AI Governance Consultant
AI Systems Tester
AI Integration Specialist
Cloud AI Engineer
Automation Engineer
Technical Consultant (AI Solutions)

Trainers

Amin Mahetar: Amin Mahetar is an accomplished Cloud Security Architect and AI practitioner with over 18 years of experience in cloud transformation, cybersecurity, and emerging technologies. He has led major digital transformation and AI-driven automation projects across the government and enterprise sectors, including roles at Cisco, GovTech Singapore, and Deutsche Bank. Amin holds multiple professional certifications, including AWS Certified Solutions Architect, CISSP, CCSP, and Google Cloud Professional Architect, reflecting his deep expertise across multi-cloud environments. His work integrates cloud security, governance, and AI solution design, enabling organizations to build secure, scalable, and intelligent applications on AWS.

In the Building Agentic AI Applications Using AWS Bedrock course, Amin brings his extensive cloud architecture experience to guide learners in designing, deploying, and securing AI-powered systems. His sessions focus on leveraging AWS Bedrock for generative AI development, integrating large language models (LLMs) into enterprise workflows, and ensuring compliance with governance and security frameworks. Learners benefit from his practical insights on building reliable, production-grade AI solutions that combine innovation with responsible deployment practices.

Mohan Pothula: Mohan Pothula is an Enterprise Architect and Data Engineering Leader with over two decades of experience designing large-scale, cloud-native solutions for financial, media, and telecommunications organizations. He has served in senior technology roles at DBS Bank, SPH Media, and Mediacorp, where he architected and implemented AI and data-driven platforms on AWS and Azure. His expertise includes AI/ML pipeline design, Kubernetes orchestration, and data architecture optimization, supported by certifications in AWS Solutions Architecture, Kubernetes Administration, and Cloud DevOps. Mohan is known for translating complex technical systems into scalable, efficient, and business-aligned architectures.

In this course, Mohan helps learners understand how to integrate generative AI capabilities into cloud-based applications using AWS Bedrock and associated services. His sessions emphasize real-world use cases, including data engineering pipelines, API-driven agent workflows, and multi-model orchestration. Participants gain hands-on experience in building, fine-tuning, and deploying intelligent agentic systems that leverage the full potential of AWS’s generative AI ecosystem.

Quah Chee Yong: Quah Chee Yong is a data science and AI specialist with over 20 years of experience in analytics, cloud computing, and applied machine learning. A certified adult educator with the WSQ ACLP qualification, he has designed and delivered AI and data analytics programs under national initiatives such as SGUnited, SAP, and IMDA. His expertise spans Python, TensorFlow, and AWS SageMaker, coupled with strong technical knowledge of data visualization, natural language processing, and model deployment. Quah is passionate about helping professionals harness the power of AI to drive innovation and productivity within their organizations.

In the Building Agentic AI Applications Using AWS Bedrock course, Quah focuses on helping learners apply generative AI concepts to develop agent-based systems using AWS tools. His sessions combine theory with hands-on projects that involve model integration, orchestration, and evaluation within the AWS ecosystem. Learners gain practical insights into designing autonomous AI workflows and deploying intelligent solutions that enhance business decision-making and automation.

Truman Ng: Truman Ng is a senior IT and AI specialist with more than 20 years of experience in cloud computing, machine learning, and intelligent automation. He holds certifications including PMP®, Huawei HCIE®, Cisco CCNP®, and AWS Certified Solutions Architect. Truman has led projects involving AI-enabled automation, DevOps integration, and multi-cloud deployment across sectors such as manufacturing, logistics, and enterprise technology. As an ACTA-certified adult educator, he has conducted professional training in AI, data analytics, and robotics, known for blending technical rigor with hands-on, real-world application.

In this course, Truman helps learners build intelligent AI agents powered by AWS Bedrock and related services such as Lambda, SageMaker, and API Gateway. His sessions focus on model deployment, workflow automation, and integration of generative AI into cloud-native systems. Through his structured and applied teaching approach, participants learn to design and implement agentic AI solutions that enhance efficiency, scalability, and innovation in enterprise environments.

Teh Siew Yee: Teh Siew Yee is an experienced professional in organizational development, workplace learning, and digital transformation. She has worked with industry leaders to design learning solutions that improve team collaboration, enhance workforce capability, and streamline operations. Her background spans people development and process improvement, giving her a strong foundation to guide learners through both technical and soft-skill growth.

As an ACLP-certified trainer, Teh specializes in delivering WSQ programs in workplace efficiency, leadership, and collaboration, integrating practical case studies with reflective exercises. Her training style emphasizes active participation and workplace relevance, ensuring learners can apply new skills directly in their roles. With her combination of organizational insights and training expertise, she equips professionals to adapt to change and contribute effectively to business strategy.

Customer Reviews (19)

Average Rating: 3.0/5 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

Overall a fast crash course. Think need more time for learners to understand. (Posted on 3/13/2026)

Average Rating: 2.0/5 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

Is very not conducive to conduct a virtual class - trainees spend 1 hour at the initial class to fix all the IT problems. The class was not properly prepared as (i) it should start at 9am but not sure why the time change to 930am on the email, (ii) many of the training emails are faulty and made us unable to login to the AWS system. Very little theory is taught, is basically following the AWS lab instruction which I don't think we need a class for this. (Posted on 3/13/2026)

Average Rating: 3.0/5 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: 4.0/5 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

Trainer is able to solve most of our problems with the lab. But the lab is a bit weird sometimes. E.g. I couldn't see the image generation on the first lab even though the trainer walked through it. (Posted on 3/13/2026)

Average Rating: 2.0/5 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

Too many technical difficulties which were difficult to resolve virtually. Unable to understand the trainer well. Course was basically following Amazon's training with little value add from the trainer. (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)