AI+ Agent™

Course code: AP1401

Price of the certification exam is included in the price of the course.

Formerly known as AI+ Agent™

Empower businesses with AI+ Agent Specialty™ to design, deploy, and scale intelligent agents.

Empower Automation with AI+ Agent Specialty™ 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

Akční cena

140 EUR

169 EUR including VAT

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Course dates
Course description
Target group
Course structure
Prerequisites
Certification

Course dates

Starting date: Upon request

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 140 EUR Akční cena

	Starting date	Type	Course duration	Language		Price without VAT
	Upon request	Self-paced	8 hours	en	A	140 EUR	Register
G	Guaranteed course
A	Akční cena

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Course description

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.

Python
LangChain
LlamaIndex
OpenAI API
Hugging Face Inference
Multi-Agent Orchestration Frameworks
Vector Databases (e.g., Pinecone, Chroma)
Workflow Orchestration (e.g., Airflow, Prefect)
Jupyter Notebooks
Docker
Prompt Engineering Platforms

Target group

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.

Course structure

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

Prerequisites

Basic understanding of AI concepts, programming knowledge in Python or similar languages, and foundational data analysis skills. Perfect for learners with a problem-solving mindset who want to apply analytical thinking to real-world AI challenges and intelligent agent development.