Artificial Intelligence is rapidly transforming the way applications are designed, developed, and deployed. Modern AI coding assistants such as Claude Code and Codex now enable users to generate code, understand entire code bases, read the documentation, or even build some from scratch, create intelligent assistants, and accelerate software development using natural language instructions (called prompts).
This highly practical and beginner-friendly course introduces learners to the fundamentals of Agentic AI application development using Claude Code and Codex. Participants will explore how AI-powered coding tools can assist in generating applications, improving productivity, creating new features, websites, and test their ideas with the help of Claude Code and Codex.
The course covers key concepts including generative AI, agentic AI, prompt engineering, AI-assisted coding, workflow automation, and responsible AI practices. Learners will also gain hands-on exposure to creating simple AI-powered applications, generating and refining code, building AI workflows, and evaluating AI-generated outputs.
Designed for both technical and non-technical professionals, this course focuses on practical use cases and real-world demonstrations to help learners confidently begin their journey into AI-powered application development and automation.
SkillsFuture for Build Agentic AI Apps with Claude Code & Codex course: Individual Singaporeans aged 25 and above can use their Skills Future Credit (Opening Credit) for this Agentic AI App Dev using Claude course training class. SkillsFuture Credit (Mid-Career) CANNOT be used for this course.
NTUC UTAP GRANT IS CURRENTLY IS NOT APPROVED YET. We will let you know once it gets approved.
Bring Your Own Device to Class: You must bring your own Windows or Mac laptop with the required admin access to install new software on your device. A paid subscription to Claude Code and Codex will be required to complete the exercises in this workshop. You can use API credits or the paid Claude Pro or Max plan, and a paid ChatGPT Plus or higher plan. The trainer will guide you in class on the type of plans and software to subscribe and install.
Practical Labs to better understand and use Claude Code and Codex Agents
This is a practical, hands-on workshop on using Claude Code & Codex for Agentic AI Coding. There will be several short & practical Lab Sessions around the topics listed below. These labs will be customized and contextualized based on class dynamics, trainee interest and trainer decisions.
Lab 1
- Install Claude Code
- Install Codex
- Configure API Keys
- Create first project
Lab 2
- Memory Management
- CLAUDE.md
- Project Instructions
- Context Engineering
Lab 3
- Commands
- Skills
- Reusable Workflows
Lab 4
- MCP Setup
- File System Access
- GitHub Integration
- External Tool Access
Lab 5
- Build Agents
- Build Subagents
- Multi-Agent Collaboration
Lab 6
- Playwright Browser Automation
- Testing and Validation
Lab 7
- Debugging and Refactoring
- Model Comparison (Claude vs OpenAI)
Lab 8
- Deploy a Complete Agentic AI Application
LU1: AI Fundamentals
T1: Underlying Principles, Core Concepts and Theories Governing Generative AI
-
- Introduction to Generative AI and Agentic AI
- Understanding LLMs and AI Coding Agents
- How Claude Code and Codex work
- AI reasoning, planning, tool use, and agentic workflows
- Foundations of AI-assisted software development
T2: Difference Between Generative and Discriminative Models
-
- Generative vs Discriminative AI Models
- AI Chatbots vs Agentic Coding Assistants
- Single-Agent vs Multi-Agent Architectures
- Agents, Subagents, and Autonomous Workflows
- Comparing Claude Code, Codex, ChatGPT, Gemini, and Copilot
T3: Demonstrate the Use of AI in Diverse Applications
-
- Installing and configuring Claude Code and Codex
- Creating and managing your first AI coding project
- Using different AI models for coding tasks
- Building a simple Agentic AI application
- Real-world software development and automation use cases
LU2: Prompt Engineering for Agentic Coding
T1: Importance of Data Quality, Preprocessing, Model Pipeline and Model Training
-
- Project context management and context engineering
- CLAUDE.md, memory files, and project instructions
- Context windows, token usage, and model limitations
- Agentic development lifecycle: Plan → Build → Test → Deploy
- Introduction to MCP (Model Context Protocol) and external tools
T2: Impact of Prompt Engineering on Model Outputs of Generative AI
-
- Effective prompting for Claude Code and Codex
- Task decomposition and planning prompts
- Custom commands and reusable workflows
- Skills, instruction files, and project templates
- Prompt refinement for generating production-quality code
T3: Apply Understanding of Generative AI Principles to Use Cases
-
- Building coding agents and subagents
- AI-assisted code generation and refactoring
- Documentation generation and code understanding
- Browser automation and testing with Playwright
- Hooks, automation triggers, and workflow orchestration
T4: Analyse Generative AI Models’ Performance Metrics and Evaluate the Influence of Prompt Variations
-
- Evaluating AI-generated code quality
- Debugging and troubleshooting AI-generated solutions
- Comparing model outputs across Claude and OpenAI models
- Measuring reliability, accuracy, and maintainability
- Deploying and validating AI-built applications
LU3: Ethical Considerations
T1: Generative AI Model Workings, Including Training Data, Algorithms, and Outputs
-
- How modern coding models are trained
- Understanding reasoning models and coding models
- Data privacy, secrets management, and secure development
- MCP security considerations and tool permissions
- Responsible use of AI-generated outputs
T2: Identify the Ethical Implications and Societal Impact of AI-Generated Content
-
- Ethical use of AI-generated code
- Human oversight and accountability
- AI collaboration within software development teams
- Impact of Agentic AI on software engineering roles
T3: Analyze Limitations and Potential Biases in AI-Generated Content
-
- Hallucinations and incorrect code generation
- Security vulnerabilities introduced by AI
- Biases and limitations of AI coding systems
- Human validation and review processes
- Best practices for trustworthy Agentic AI development
By the end of this course, learners will be able to:
- Understand Generative AI, Agentic AI and AI coding assistants.
- Use Claude Code and Codex to support AI-powered application development.
- Create simple AI-powered applications using natural language prompts.
- Generate, review and refine AI-generated code.
- Build AI assistants, chatbots and productivity tools.
- Apply prompt engineering techniques for better AI outputs,
- Evaluate AI-generated content and code for accuracy, quality, and reliability,
- Understand the limitations, risks, and ethical considerations associated with AI-generated outputs,
- Identify practical workplace and business use cases for Agentic AI technologies
Participants are not required to have prior programming or AI development experience.
However, learners should ideally have:
- Basic computer literacy
- Familiarity with using web applications and online tools
- Basic understanding of business processes or workflows
- Interest in AI, automation, or application development
Prior exposure to coding, scripting, or generative AI tools such as ChatGPT or Claude would be beneficial but is not mandatory.
-
This course is suitable for:
- Business professionals exploring AI tools and automation in coding and web or app development
- Executives and managers interested in building apps without writing a single line of code
- Data analysts and power users to analyze huge amounts of data in Excel, Text files, or other formats quickly.
- IT professionals and system administrators to accelerate the product development and enhancement process
- Beginner developers and aspiring AI developers to master the AI Tools that are fast evolving
- Process improvement and operations teams who do not have to rely on expensive and slow IT work.
- Innovation and digital transformation teams to speed up the development of new product features and accelerate go to market strategies to implementation.
- Educators, trainers, and consultants who want to leverage on Claude Code & Codex for designing next gen cutting edge code without actually coding!
- Anyone interested in learning how AI coding assistants can help build intelligent applications and websites, analyze data and do much more.
Post-Course Support
- We provide free consultation related to the subject matter after the course.
- Please email your queries to training@intellisoft.com.sg and we will forward your queries to the subject matter experts.
Venue:
All courses are conducted at Intellisoft Training Rooms at 190 Middle Road, 10-08 Fortune Centre, Singapore 188979.
Short walk from Bencoolen MRT, Bugis, Rochor, Bras Basah MRT stations. The venue is disabled-friendly. For directions, click Contact Us.
By the end of this course, learners will be able to:
- Understand Generative AI, Agentic AI and AI coding assistants.
- Use Claude Code and Codex to support AI-powered application development.
- Create simple AI-powered applications using natural language prompts.
- Generate, review and refine AI-generated code.
- Build AI assistants, chatbots and productivity tools.
- Apply prompt engineering techniques for better AI outputs,
- Evaluate AI-generated content and code for accuracy, quality, and reliability,
- Understand the limitations, risks, and ethical considerations associated with AI-generated outputs,
- Identify practical workplace and business use cases for Agentic AI technologies
-
This course is suitable for:
- Business professionals exploring AI tools and automation in coding and web or app development
- Executives and managers interested in building apps without writing a single line of code
- Data analysts and power users to analyze huge amounts of data in Excel, Text files, or other formats quickly.
- IT professionals and system administrators to accelerate the product development and enhancement process
- Beginner developers and aspiring AI developers to master the AI Tools that are fast evolving
- Process improvement and operations teams who do not have to rely on expensive and slow IT work.
- Innovation and digital transformation teams to speed up the development of new product features and accelerate go to market strategies to implementation.
- Educators, trainers, and consultants who want to leverage on Claude Code & Codex for designing next gen cutting edge code without actually coding!
- Anyone interested in learning how AI coding assistants can help build intelligent applications and websites, analyze data and do much more.
LU1: AI Fundamentals
T1: Underlying Principles, Core Concepts and Theories Governing Generative AI
-
- Introduction to Generative AI and Agentic AI
- Understanding LLMs and AI Coding Agents
- How Claude Code and Codex work
- AI reasoning, planning, tool use, and agentic workflows
- Foundations of AI-assisted software development
T2: Difference Between Generative and Discriminative Models
-
- Generative vs Discriminative AI Models
- AI Chatbots vs Agentic Coding Assistants
- Single-Agent vs Multi-Agent Architectures
- Agents, Subagents, and Autonomous Workflows
- Comparing Claude Code, Codex, ChatGPT, Gemini, and Copilot
T3: Demonstrate the Use of AI in Diverse Applications
-
- Installing and configuring Claude Code and Codex
- Creating and managing your first AI coding project
- Using different AI models for coding tasks
- Building a simple Agentic AI application
- Real-world software development and automation use cases
LU2: Prompt Engineering for Agentic Coding
T1: Importance of Data Quality, Preprocessing, Model Pipeline and Model Training
-
- Project context management and context engineering
- CLAUDE.md, memory files, and project instructions
- Context windows, token usage, and model limitations
- Agentic development lifecycle: Plan → Build → Test → Deploy
- Introduction to MCP (Model Context Protocol) and external tools
T2: Impact of Prompt Engineering on Model Outputs of Generative AI
-
- Effective prompting for Claude Code and Codex
- Task decomposition and planning prompts
- Custom commands and reusable workflows
- Skills, instruction files, and project templates
- Prompt refinement for generating production-quality code
T3: Apply Understanding of Generative AI Principles to Use Cases
-
- Building coding agents and subagents
- AI-assisted code generation and refactoring
- Documentation generation and code understanding
- Browser automation and testing with Playwright
- Hooks, automation triggers, and workflow orchestration
T4: Analyse Generative AI Models’ Performance Metrics and Evaluate the Influence of Prompt Variations
-
- Evaluating AI-generated code quality
- Debugging and troubleshooting AI-generated solutions
- Comparing model outputs across Claude and OpenAI models
- Measuring reliability, accuracy, and maintainability
- Deploying and validating AI-built applications
LU3: Ethical Considerations
T1: Generative AI Model Workings, Including Training Data, Algorithms, and Outputs
-
- How modern coding models are trained
- Understanding reasoning models and coding models
- Data privacy, secrets management, and secure development
- MCP security considerations and tool permissions
- Responsible use of AI-generated outputs
T2: Identify the Ethical Implications and Societal Impact of AI-Generated Content
-
- Ethical use of AI-generated code
- Human oversight and accountability
- AI collaboration within software development teams
- Impact of Agentic AI on software engineering roles
T3: Analyze Limitations and Potential Biases in AI-Generated Content
-
- Hallucinations and incorrect code generation
- Security vulnerabilities introduced by AI
- Biases and limitations of AI coding systems
- Human validation and review processes
- Best practices for trustworthy Agentic AI development
Participants are not required to have prior programming or AI development experience.
However, learners should ideally have:
- Basic computer literacy
- Familiarity with using web applications and online tools
- Basic understanding of business processes or workflows
- Interest in AI, automation, or application development
Prior exposure to coding, scripting, or generative AI tools such as ChatGPT or Claude would be beneficial but is not mandatory.
Post-Course Support
- We provide free consultation related to the subject matter after the course.
- Please email your queries to training@intellisoft.com.sg and we will forward your queries to the subject matter experts.
SkillsFuture Ready
Singaporeans can use $500 SkillsFuture Credits for this training to offset the course fees.
Contact us for advise on how to go about claiming your SkillsFuture.
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How To Register
Register for the 2 Days Build Agentic AI Apps with Claude Code & Codex course in Singapore today.
Click on the chosen course date for Building Agentic AI Apps with Claude Code & Codex. Training course dates are available at the top of this page.
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Build Agentic AI Apps with Claude Code & Codex Corporate Training Program in Singapore (Physical Class or Virtual Classroom using Zoom or Microsoft Teams)
Request a Course Brochure here or enquire about any questions regarding Agentic AI Coding training. We offer Corporate Training for your company too.
If you are looking to train your entire team, we can arrange to conduct the Claude Code training at your office location too. Just contact us for the quotation and suitable dates for the training.
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