Description

The Agentic AI Bootcamp at Coder Faculty is a practical 16-week programme designed for both students and professionals who want to build strong skills in Agentic AI, intelligent automation and modern AI systems. This course provides a structured introduction to AI agents, workflow automation, tool-connected systems and practical agent development using real-world business and technical applications.

The programme begins with the foundations of Agentic AI, introducing students to modern AI agents, workflow thinking, prompt engineering and structured task design. It also includes essential Python programming for AI and automation, giving students the ability to work with structured data, APIs and reusable automation scripts.

As the course progresses, the curriculum introduces modern tools and platforms such as n8n, Ollama, LM Studio, Cursor, Codex, Antigravity, Claude Code and OpenCode. Students learn how to design practical AI workflows, connect agents to APIs, documents and external services and build systems that can reason, use tools and automate tasks. The course also covers key topics such as tool calling, Retrieval-Augmented Generation (RAG), the Model Context Protocol (MCP), memory, orchestration and human-in-the-loop design.

A key feature of this bootcamp is its focus on practical implementation of AI agents. Throughout the 12 weeks, students work on hands-on labs and real-world agentic AI tasks including workflow automation, document-aware assistants, tool-connected agents and business process automation. These practicals help students build a strong portfolio of projects that demonstrates their readiness for work in Agentic AI and automation.

The programme ends with a capstone project, technical review, certification assessment and career guidance session. These elements ensure that students gain both a strong theoretical foundation and practical implementation skills, preparing them for emerging opportunities in Agentic AI, AI automation and intelligent systems development.

Register for the course

What you'll learn

Become a Certified Agentic AI Practitioner

Understand modern AI agents, agentic workflows and intelligent systems

Design and build practical AI automation workflows

Work with tools such as n8n, Ollama, LM Studio, Cursor, and Codex

Build AI systems that can reason, use tools and automate tasks

Understand prompt engineering, task design and structured outputs

Explore memory, state, orchestration and human-in-the-loop agent design

Understand multi-agent systems, handoffs and agent interoperability concepts

Learn evaluation, observability, guardrails and secure AI deployment practices

Understand core Python programming concepts for AI automation

Develop practical skills in low-code and code-based AI workflow design

Connect AI agents with APIs, documents and external services

Learn Retrieval-Augmented Generation (RAG) for grounded and reliable AI systems

Gain hands-on experience with real-world agentic AI projects

Understand the Model Context Protocol (MCP) and modern AI tool integration concepts

Gain practical experience deploying AI assistants, workflows and automation solutions

Prepare for emerging career opportunities in Agentic AI, AI automation and intelligent workflow engineering

Why should you take this course?

This bootcamp offers an intensive, hands-on learning experience for students, professionals and innovators who want to build practical skills in Agentic AI, intelligent automation and AI workflow design. With its blend of modern AI theory and real-world practical projects, the Agentic AI Bootcamp at Coder Faculty provides a reliable career path into the fast-evolving world of AI agents, automation and intelligent systems. By the end of the bootcamp, students will have developed a highly sought skillset that will open up new career opportunities in AI, automation and digital transformation.

How is the course delivered?

  • Module 1: Agentic AI Foundations - Learn the fundamentals of Agentic AI, intelligent automation, workflow design, prompt engineering, structured outputs and Python basics for agent development.
  • Module 2: Agent Development and Automation - Build practical agentic systems using n8n, Python, APIs, local AI tools, workflow platforms and tool calling.
  • Module 3: Advanced Agents and Deployment - Explore RAG, MCP, memory, orchestration, multi-agent systems, browser agents, guardrails, evaluation and deployment of real-world agentic AI systems.

How is the course delivered?

  • A qualified lecturer will deliver each session in real time, so you’re always interacting with a live instructor rather than just watching a recording.
  • Students can attend classes in-person at our Curepipe campus in Mauritius or join online from anywhere.
  • This course is delivered on a part-time basis over weekends (Saturdays) to accommodate working professionals and students with busy weekday schedules.
  • All classes are recorded. If you miss a session or want to review a topic, the recording will be posted on our platform for later viewing. If you are not always available on the session date, you can register and view the recordings after the session and progress through the course at your own pace.
  • Every session blends concise theory, live demonstrations, hands‑on practicals and a Q&A segment to keep you engaged.

Why should you take this course?

This bootcamp offers an intensive, hands-on learning experience for students, professionals and innovators who want to build practical skills in Agentic AI, intelligent automation and AI workflow design. With its blend of modern AI theory and real-world practical projects, the Agentic AI Bootcamp at Coder Faculty provides a reliable career path into the fast-evolving world of AI agents, automation and intelligent systems. By the end of the bootcamp, students will have developed a highly sought skillset that will open up new career opportunities in AI, automation and digital transformation.

Certificates

  • A certificate of course completion will be awarded to all students who successfully complete the course.
  • Students will also have the opportunity to earn 2 globally recognised certifications at no additional cost.
  • An additional certificate from Harvard University is available to students who complete additional coursework and pass the required exams.

Requirements

  • There are no requirements for this course.
  • Anyone can take this course as it is designed for both beginners and professionals.
  • The course starts with the basics and gradually progresses to advanced topics, ensuring that all students can follow along.
  • This course is suitable for individuals from diverse backgrounds, including students, working professionals, and career changers with no prior experience in AI or IT


Register for this course and become a certified Agentic AI engineerw today!

Course Content

16 weeks
Induction and Development Environment Setup for Agentic AI
Session 1 • 3 hours
Course introduction, structure and capstone overview
Overview of the Agentic AI ecosystem
Installing and configuring Visual Studio Code, Cursor and Antigravity
Installing Python and setting up a local development environment
Installing Git and configuring a GitHub workflow
Installing Ollama and LM Studio for local model execution
Setting up Gemini and OpenRouter for API access
Setting up n8n for workflow automation
Setting up Opencode or Claude Code for code-based agent development
Managing API keys, environment variables and secure configuration
Practical setup task: validate the local environment by running a model, creating a repository and executing a basic workflow
Agentic AI Fundamentals and System Design
Session 2 • 3 hours
Definition and characteristics of agentic systems
Assistants, workflows, agents and multi-agent systems
The agent loop: planning, acting, observing and revising
Autonomy, control and human-in-the-loop design
Identifying suitable use cases for agentic systems
Mapping business and technical processes into agent workflows
Failure modes and limitations of agentic systems
Scenario study: support triage, document processing and reporting workflows
Practical exercise: convert a real process into an agent design
Prompt Engineering, Task Decomposition and Structured Outputs
Session 3 • 3 hours
Prompt engineering for operational systems
Task decomposition and stepwise design
Instructions, constraints and success criteria
Structured outputs and JSON schema design
Prompt patterns for extraction, classification, routing and drafting
Controlling ambiguity and reducing hallucination
Prompt chaining and reusable prompt templates
Practical exercise: design prompts for email routing, action extraction and complaint classification
Scenario task: convert a standard operating procedure into structured agent instructions
Python Programming Basics for Agentic AI
Session 4 • 3 hours
Introduction to Python for AI and automation
Variables, data types and operators
Conditional statements and loops
Functions and reusable utility scripts
Working with lists and dictionaries
Basic input and output
Practical exercise: build a simple Python script for structured data handling
Python for Agentic AI: Data Handling, APIs and Automation
Session 5 • 3 hours
Python essentials required for agent development
Libraries and package management with pip
Understanding JSON structures in Python
Reading and writing local files
Calling external APIs with Python
Working with webhooks and service responses
Exception handling and debugging in automation scripts
Practical exercise: build a Python utility that transforms structured data and interacts with an API
Scenario task: develop a small automation script for notification dispatch or record enrichment
Tool Calling, Function Schemas and Model Context Protocol
Session 6 • 3 hours
Tool calling and function calling in agent architectures
Designing function schemas and controlled actions
Connecting agents to files, APIs and external services
Introduction to the Model Context Protocol (MCP)
MCP tools, resources, prompts, clients and servers
Direct integrations compared with MCP-based integrations
Interoperability considerations in tool-connected systems
Practical exercise: define tools for document lookup, task creation and outbound notification
Scenario task: design an agent workflow that retrieves information and updates an external system
Workflow Automation with n8n
Session 7 • 3 hours
Workflow automation concepts and architecture
Triggers, actions, branching logic and retries in n8n
Using AI components inside workflow pipelines
Connecting forms, email, spreadsheets and APIs
Approval steps and controlled execution paths
Error handling and workflow robustness
Practical exercise: implement an email triage workflow with classification and notification
Scenario task: design workflows for lead qualification, invoice routing and internal request handling
Retrieval-Augmented Generation and Knowledge-Grounded Agents
Session 8 • 3 hours
Why agent systems require grounded knowledge
Retrieval-Augmented Generation (RAG) fundamentals
Chunking, indexing, retrieval and context injection
Document-aware assistants for policies, procedures and internal knowledge
Grounded responses, source attribution and evidence handling
Reliability considerations in knowledge-based systems
Practical exercise: build a document-grounded assistant over a handbook or policy set
Scenario task: develop an assistant for HR policies, student support or logistics procedures
Memory, State and Human-in-the-Loop Workflows
Session 9 • 3 hours
Short-term context and long-term memory
State management in multi-step workflows
Persistent and resumable agent processes
Approval logic, checkpoints and escalation paths
Confidence thresholds and exception routing
Designing semi-autonomous systems with human oversight
Practical exercise: add memory and approval logic to an existing workflow
Scenario task: model procurement approval, recurring reporting and customer follow-up workflows
Multi-Agent Systems, Handoffs and Inter-Agent Coordination
Session 10 • 3 hours
Single-agent and multi-agent architectural patterns
Planner-executor, reviewer and supervisor patterns
Agent handoffs and delegation mechanisms
Specialist agents and task partitioning
Introduction to interoperability and A2A concepts
Complexity management in collaborative agent systems
Practical exercise: redesign one workflow as both a single-agent and multi-agent architecture
Scenario task: implement collaborative flows for research, proposal generation or compliance review
Browser Agents, Computer Use and Interface-Level Automation
Session 11 • 3 hours
API-based automation compared with browser-driven automation
Computer-use agents and interface-level execution
Automating tasks across websites, dashboards and administrative portals
Failure points in UI-dependent workflows
Oversight, permissions and recovery strategies
Practical exercise: map a browser-based workflow and identify fragile steps and control points
Scenario task: design an automation for an administrative portal, dashboard extraction workflow or booking system
Deployment, Capstone Integration and Technical Review
Session 12 • 3 hours
Preparation of agent systems for deployment
Local, cloud and hybrid deployment models
Deploying assistants, workflows and internal tools
Monitoring, maintenance and lifecycle management
Capstone project integration and refinement
Presenting architecture, implementation choices and technical trade-offs
Technical review of completed systems
Practical exercise: prepare a deployment plan, architecture summary and evaluation report for the capstone
Evaluation, Observability, Guardrails and Secure Agent Design
Self paced session • 3 hours
Evaluation objectives for agent systems
Scenario-based testing and regression testing
Assessing correctness, latency, cost and reliability
Tracing, logs and observability in agent workflows
Guardrails, policy checks and output validation
Prompt injection basics and defensive design patterns
Data privacy and secure action design
Practical exercise: test an agent workflow across normal and failure scenarios
Scenario task: harden a workflow involving sensitive responses, approvals or internal policy guidance
Self Learning: Custom Python Tools and Reusable Utilities
Self paced session • 3 hours
Building reusable Python tools for agent workflows
Creating utility functions for structured automation tasks
Working with APIs, webhooks and structured outputs
Mini exercises for custom integrations
Self Learning: Emerging Standards, Agent UX and Future Directions
Self paced session • 3 hours
Interoperability trends and protocol ecosystems
Agent UX and approval interface design
Enterprise adoption patterns for agent systems
Future directions in AI automation and digital workers

Instructor

N.Rampersand
N.Rampersand

Instructor

Nirmal Rampersand is an accomplished Lead Software Engineer with extensive experience in training and leading software development teams with agile frameworks. With a diverse skill set spanning multiple programming languages and domains, he is proficient in Python, Node.js, Java, PHP, SQL, NoSQL, C++, Machine Learning (ML), and Natural Language Processing (NLP). He is currently conducting doctoral research in the field of Computational Intelligence and Optimisation.

With several years of dedicated teaching experience, Nirmal has consistently demonstrated his passion for nurturing the next generation of software developers and data scientists. He has successfully trained both students and professionals in a wide array of domains, including Python programming, Full Stack Web Development, and Data Science & Machine Learning. As a Udemy instructor, he has harnessed his expertise to develop comprehensive curriculums and on-demand courses. This has allowed him to reach a global audience of learners, empowering them with the knowledge and skills necessary to succeed in today's fast-paced tech industry.

He also excelled as a Freelance Software Engineer, demonstrating mastery in developing robust REST APIs using Node.js and Python. His innovative flair was evident in creating intelligent systems for NLP solutions and complex chatbots. Furthermore, he is an expert in data mining and analytics. His proficiency extends to application development, covering the entire lifecycle, and he's crafted user-friendly, cross-platform mobile applications. These hands-on experiences uniquely qualify him to impart real-world knowledge to students and professionals alike.

These rich experiences in software engineering, development, and AI have uniquely positioned him as an educator who not only imparts knowledge but also shares real-world, hands-on expertise with his students.

Student Reviews

  • Aswad Banee - Software Engineer at Agileum

    Rating: 5/5

    It was a fantastic training, and you got fantastic teaching skills. This made it possible for me to quickly pick up python. Every single one of your lectures was excellent. I sincerely appreciate it.

  • Aditya Potharala - Software Engineering Student

    Rating: 5/5

    The course covers a lot of material and also the assignments were manageable. It was interactive, informative and well planned. It has been a truly rewarding learning experience and the tutor was very understanding and supportive throughout the course. Highly recommended !

  • Altaaf Waresh Allee - Junior Developer

    Rating: 5/5

    Courses are well structured, lectures are well explained and practical sessions are made easy for anyone. Mr Nirmal a really great lecturer, one who shows passion for his job and cares for his students. Really enjoyed the efforts made for students.



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