Course Overview

The AI+ Agent certification is a beginner-friendly program that teaches you how to design and deploy autonomous agents capable of independent decision-making and task execution. Through an immersive, action-oriented curriculum, you will use real-world workflows and guided projects to build high-performance agents that scale business operations. This course empowers you to transition from simple AI interactions to implementing sophisticated, intelligent automation that drives organizational efficiency.

Course Objectives

• Industry Recognition: A specialized credential that signals strong capability in AI agent design, deployment, and management.
• Career Differentiation: A standout addition to your profile that highlights expertise in AI-powered automation and intelligent workflows.
• Hands-on Proficiency: Demonstrated experience using agent-building tools, frameworks, and best practices to solve real problems.
• Business Value Creation: Proven ability to build agents that streamline operations, elevate customer experiences, and drive measurable ROI.
• Future-ready Skill Set: Strong alignment with the growing demand for AI agents across industries, keeping your skills relevant and competitive.

Who Should Attend?

• Aspiring AI Professionals: Learners looking to break into AI by building practical experience with intelligent agents and automation.
• Software Developers & Engineers: Python or similar language programmers who want to design, integrate, and deploy AI agents into real applications.
• Data Analysts & Data Scientists: Professionals who work with data and want to operationalize insights through AI-driven agents and workflows.
• Product Managers & Tech Leaders: Decision-makers aiming to understand, plan, and oversee AI agent solutions that enhance products and services.
• Automation & Operations Specialists: Those focused on process optimization who want to replace repetitive tasks with smart, autonomous AI agents.

Pre-requisite

• Basic Understanding of AI Concepts – Familiarity with core AI principles.
• Programming Knowledge – Proficiency in Python or similar languages.
• Data Analysis Skills – Ability to interpret and manipulate datasets.
• Problem-Solving Mindset – Analytical thinking to address AI challenges.
• Familiarity with Machine Learning – Understanding basic ML algorithms and techniques.

Course Outline

Module 1: Introduction to AI Agent

1.1 Understanding AI Agents

1.2 Anatomy and Ecosystem of AI Agents

1.3 Applications, Misconceptions, and Mini Case Studies

1.4 Case Study: Transforming Customer Support at Acme Retail with AI Agents

1.5 Hands-On Exercise 1: Build a Q&A ChatBot Using Gemini + Prompt + LLM Chain in Flowise Cloud

Module 2: Core Concepts & Types of AI Agents

2.1 Anatomy of an AI Agent

2.2 Classification of AI Agents

2.3 Matching Agents to Use Cases

2.4 Case Study: Enhancing Mental Health Support with AI Agents at Earkick

2.5 Hands-On Exercise

Module 3: Tools for Non-Coders

3.1 No-code and visual agent platforms

3.2 Tools Overview and Setup

3.3 Start building: “Your First Flow” with n8n

3.4 Case Study: Empowering HR with AI – Building an Onboarding Assistant Without Coding

3.5 Hands-on Exercise

Module 4: Building Simple Agents

4.1 Agent

4.2 Agent 2

4.3 Agent 3

4.4 Agent 4

4.5 Troubleshooting and Validation of AI Agents

4.6 Share Your AI Agent

4.7 Hands-On Exercise 1

Module 5: Multi-Tool Agents and Workflow Automation

5.1 Multi-Tool Agent

5.2 Agent Chaining and Workflow Basics

5.3 Managing Agent State: State, Context, and User Journey

5.4 Prompt Engineering for Agents

5.5 Multi-Agent Systems (MAS)

5.6 Case Study: Smarter Marketing Campaigns with Tool Chaining

5.7 Hands-on Exercise: Automating Order Tracking and Notifications with Make.com

Module 6: Integration, Application Mapping & Deployment

6.1 Deploying Agents

6.2 Channel Selection – Where the User will Interact

6.3 Hosting Environment – Where does the Agent Run?

6.4 Data Integration

6.5 Security Setup

6.6 Monitoring & Updates

6.7 Application Mapping

6.8 Hands-on Exercise 1: Integration of a Portfolio Assistant Chatbot into GitHub Pages using Zapier

Module 7: Monitoring, Guardrails & Responsible AI

7.1 Observability Basics

7.2 Performance Evaluation: Key Metrics

7.3 Guardrails: Preventing Misuse & Ensuring Safe Outputs

7.4 Responsible AI

7.5 Mini-Case: Failure and Recovery in Agent Deployments

7.6 Real-world Failures

7.7 Peer Sharing: How to Present and Discuss Agent Logs/Results

Module 8: Capstone Project – Design Your Own Intelligent Agent

8.1 Capstone Project 1: Smart Personal AI Assistant

8.2 Capstone Project 2: Smart Lead Engagement – From Email to Personalized Outreach – Sales Support Agent

8.3 Capstone Project 3: Education Tutor Agent

8.4 HR Knowledge Bot

8.5 Customer Service Agent

8.6 Healthcare Triage Bot