Course Information

  • Sessions 4 days
  • Duration 32 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.

Skills Framework

TSC Title
: Generative AI Application Development and Deployment
TSC Code
ICT-INT-0047-1.1

Learning Outcomes

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

  • LO1: Design multi-agent integration workflows using CrewAI, Autogen, and ADK for intelligent AI application development.
  • LO2: Develop and deploy Agentic AI and RAG applications on Streamlit and cloud-based platforms.
  • LO3: Deploy guardrails to ensure safe and aligned outputs in Agentic AI applications.
Download Course Brochure

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 - Build and Deploy Agentic AI Apps with CrewAI, Autogen, ADK and Streamlit

Course Code: TGS-2025059028
  • WSQ
  • SkillsFuture Credit
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES

What's This Course About

This course provides learners with a comprehensive understanding of how to build and deploy cutting-edge Agentic AI applications using CrewAI, Autogen, ADK, and Streamlit. Participants will gain practical skills in designing multi-agent workflows, integrating these frameworks for intelligent and context-aware AI applications. The course covers best practices for deploying these applications on cloud-based platforms and introduces retrieval-augmented generation (RAG) techniques to boost application effectiveness. Learners will also explore essential strategies for deploying guardrails to ensure that outputs are safe, aligned, and compliant with ethical AI practices.

In today’s rapidly evolving AI landscape, these skills are highly relevant and sought-after. Proficiency in building multi-agent AI systems and deploying them effectively on popular frameworks opens up exciting career paths in AI engineering, data-driven product development, and machine learning infrastructure. Moreover, the inclusion of model alignment and guardrail strategies prepares learners to create AI applications that meet high standards of safety and responsibility, a key differentiator in modern AI-driven businesses. Completing this course can significantly enhance job prospects and support transitions into more advanced technical roles.

This course is designed for beginner to intermediate learners who have some experience with AI concepts and basic coding, and are looking to expand their knowledge into multi-agent workflows and advanced AI deployment..

WSQ Funding

Full Fee $2,000.00 Before GST
GST $180.00 9% of fee
Baseline Nett $1,180.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $780.00 SG age 40+ · 70% funded
WSQ Funding
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-2025059028)
  • 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 FeeBefore Funding

$2,000.00 (GST-exclusive)
$2,180.00 (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

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

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

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:

TBD

Hardware: Window or Mac Laptops

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

Tan Woei Ming: Tan Woei Ming is a robotics and artificial intelligence educator with extensive experience in advanced automation, programming, and applied AI systems. 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. His practical expertise bridges machine intelligence and engineering, equipping learners to understand both the hardware and software layers of AI-driven systems. He brings a hands-on, systems-thinking approach to the Agentic AI Apps curriculum, guiding participants in integrating CrewAI, Autogen, and Streamlit into functional agent pipelines. With strong roots in real-world robotic control and adaptive automation, Tan helps learners translate theoretical AI concepts into deployable, multi-agent applications that interact with physical or simulated environments. Teh Siew Yee: Teh Siew Yee is a data science and digital transformation leader with over 25 years of global industry and teaching experience across technology, finance, and aerospace sectors. He has led enterprise-wide AI adoption initiatives, built analytics teams from the ground up, and served as Head of Data Engineering & Analytics at SIA Engineering Company and Senior Data Specialist at Standard Chartered Bank. With postgraduate training in Artificial Intelligence from Singapore Management University, his expertise spans machine learning, blockchain, and business intelligence. In the Agentic AI Apps course, Teh Siew Yee integrates his experience in responsible AI and data strategy to teach how intelligent agents leverage analytics, automation, and orchestration frameworks like Autogen and CrewAI. His strong foundation in Python, Tableau, and cloud platforms ensures participants gain practical skills to design, monitor, and deploy agentic applications within enterprise-grade ecosystems. Truman Ng: Truman Ng is a certified AI and network engineering professional with a multidisciplinary portfolio spanning machine learning, blockchain, cybersecurity, and automation. A graduate in Electrical and Electronic Engineering from NUS and a PMP®-certified project manager, he holds multiple credentials including Huawei HCIE®, Cisco CCNP, and Certified Bitcoin Professional (CBP). His teaching portfolio covers Python, TensorFlow, Docker, CI/CD pipelines, and RPA systems, reflecting deep competence in both software engineering and IT infrastructure. Leveraging his cross-domain expertise, Truman connects the Agentic AI Apps curriculum to real-world deployment pipelines—from model orchestration to containerized agent systems. His sessions emphasize modular design, security, and scalability, preparing learners to deploy CrewAI- and Autogen-based applications efficiently across cloud or on-premise environments. James Lee Kin Nam: James Lee Kin Nam is an Adobe Certified Expert / Instructor and seasoned digital design professional with over 20 years of training experience in creative technologies and web development. His background spans content creation, UX/UI design, and digital marketing, with teaching engagements at Singapore Polytechnic, ACE Training, and NTU. Proficient in HTML5, CSS3, JavaScript, and WordPress, James has led courses integrating design thinking with coding and analytics for digital innovation. In the context of the Agentic AI Apps course, he focuses on user-centric design and front-end deployment using Streamlit. By combining creative visualization with technical implementation, James empowers learners to present AI agent outputs through intuitive dashboards and interactive web interfaces, bridging the gap between data science, automation, and user experience design. Dr. Alfred Ang: Dr. Alfred Ang is a leading expert in AI-driven automation, agentic AI, and workforce transformation with over 20 years of experience in industry, research, and adult education. As Chief Instructional Designer, Chief Technology Officer, and Chief Information Officer of Tertiary Infotech Pte Ltd, he has developed more than 500 WSQ- and IBF-accredited courses, integrating cutting-edge technologies into practical training for enterprises and professionals. Holding a PhD from the National University of Singapore and advanced certifications including PMP®, CSM®, AWS Certified AI Practitioner, Microsoft Azure AI Engineer, and SCS Certified Senior AI Ethics Professional, Dr. Ang has deep expertise in building AI-powered applications and automation workflows. His applied projects include agentic AI-driven curriculum generation, multimodal AI platforms, and LLM-powered robotics systems, where he has successfully deployed solutions using frameworks such as Autogen, CrewAI, ADK, and Streamlit to bridge research innovation with real-world applications Beyond technical leadership, Dr. Ang has extensive experience mentoring university and polytechnic interns, consulting on workplace learning adoption, and guiding organizations in integrating agentic AI into their business processes. His teaching emphasizes hands-on application of agentic AI development frameworks, enabling learners to design, build, and deploy AI-powered apps without requiring deep programming expertise. With his strong foundation in both curriculum design and applied AI development, Dr. Ang equips participants with the knowledge and tools to leverage CrewAI, Autogen, ADK, and Streamlit effectively—transforming ideas into scalable, real-world AI applications. His blend of academic depth, practical innovation, and pedagogical expertise makes him an ideal facilitator for the WSQ – Build and Deploy Agentic AI Apps programme

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