AI+ Agent™

Empower Automation with AI+ Agent™ for intelligent, efficient task execution

Beginner-Friendly Pathway: Perfect for learners stepping into the world of AI agents, offering simple, structured guidance for confident skill-building
Immersive Learning Experience: Combines essential AI agent fundamentals, intuitive tools, and real-world workflows to help you understand, build, and deploy automated agents
Action-Oriented Skill Development: Features practical exercises, scenario-based tasks, and guided projects so you can design, optimise, and showcase high-performance AI agents with ease

At a Glance: Course + Exam Overview

Category	AI Professional AI Specialization AI Agent Developer All Courses English Language
Program Name	AI+ Agent™
Duration	Instructor-Led: 1 Day Self-Paced: 8 hours of content
Prerequisites	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.
Exam Format	50 questions, 70% passing, 90 minutes

What You'll Learn

Agent Foundations:
Understand AI agent fundamentals, architectures, and real-world use cases across different industries and workflows.
Agent Design & Building:
Design and build task-oriented and conversational agents using modern frameworks, tools, and APIs.
Multi-Agent Orchestration:
Orchestrate multi-agent systems that collaborate, share context, and handle complex, end-to-end workflows.
Performance & Optimization:
Implement monitoring, evaluation, and optimization strategies to improve agent performance, reliability, and user experience.
Responsible Deployment:
Apply best practices for secure, ethical, and human-in-the-loop deployment of AI agents in production environments.

Certification Modules

Module 1: Introduction to AI Agents

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 1
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 Agents
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

Industry Opportunities

AI Agent Solutions Architect
Design and implement end-to-end AI agent ecosystems that automate complex workflows and integrate seamlessly with enterprise systems.
Conversational & Virtual Agent Designer
Create intelligent customer-facing agents that handle inquiries, personalize interactions, and enhance user experiences across digital channels.
Automation & Operations Lead
Oversee deployment, monitoring, and optimization of AI agents to ensure reliability, scalability, and continuous improvement in automated processes.
AI Agent Product Manager
Define strategy, roadmap, and performance metrics for agent-based products and platforms that drive business value and innovation.
Head of Intelligent Agent Transformation
Lead organization-wide adoption of AI agents, aligning technology, teams, and processes to enable scalable, autonomous operations.

Frequently Asked Questions

Yes, this certification is highly practical, focusing on building and deploying AI agents for real workflows. You’ll be able to apply agent-based automation directly to business processes, customer journeys, and internal operations.

This certification focuses specifically on intelligent agent design, orchestration, and deployment—going beyond theory to show how agents can act, decide, and collaborate across tools, apps, and systems in real business environments.

You’ll build task-oriented and conversational agents, multi-agent workflows, tool-using agents, and process-automation solutions—mirroring real organizational use cases like support automation, internal copilots, and workflow agents.

The course combines expert-led lessons, guided labs, and project-based learning where you design, configure, and deploy agents end-to-end, ensuring you gain hands-on, implementation-ready skills—not just conceptual knowledge.

It equips you with in-demand skills in agent building, orchestration, and automation, along with a portfolio of agent projects that align with emerging roles in AI engineering, automation, and intelligent systems design.

Prerequisites

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.

Exam Details

Duration

90 minutes

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

Exam Blueprint

Introduction to AI Agents	7%
Core Concepts and Types of AI Agents	15%
Tools for Non-Coders	15%
Building Simple Agents	15%
AI Agent Builder	12%
Integration, Application Mapping & Deployment	12%
Monitoring, Guardrails & Responsible AI	12%
Capstone Project - Design Your Own Intelligent Agent	12%