Introduction to Agentic AI
This course introduces the fundamentals of AI agents through a rich mix of interactive content. Learn what AI agents are, how they function, and why they are shaping the future of technology. Engage with an AI-driven podcast that responds to your questions, watch informative videos, explore curated readings, and check your understanding with short quizzes. Perfect for anyone looking to build a strong foundation in agentic artificial intelligence.

Language
- English
Topic
- Artificial Intelligence
Skills You Will Learn
- Artificial Intelligence, Agentic AI, AI Agent, Generative AI, LLM
Offered By
- IBMSkillsNetwork
Estimated Effort
- 3 hours
Platform
- SkillsNetwork
Last Update
- July 15, 2025
What You’ll Learn
- Explain the core principles of agentic AI and distinguish it from generative AI models.
- Describe how AI agents interact with tools and services using protocols like MCP to perform complex tasks autonomously.
- Analyze the structure and function of orchestrator agents and how they coordinate multiple agents to complete workflows.
- Evaluate the benefits, limitations, and risks of deploying agentic AI in real-world environments and multi-agent systems.
Course Overview
- Explore how agentic AI differs from generative AI and why this matters in real-world applications
- Understand what makes an AI system an “agent” and how agents operate independently
- Examine how agentic systems compare to traditional AI workflows and automation tools
- Learn about different categories of AI agents and the roles they play
- Identify practical scenarios where agentic AI is useful—and where it may not be the best fit
- Discover how AI agents enhance their capabilities by interacting with external tools and services
- See how agents can perform tasks like querying databases using real-world examples such as SQL agents
- Understand the Model Context Protocol (MCP) and how it enables seamless tool integration
- Learn when it’s appropriate for agents to call tools automatically versus when human oversight is needed
- Understand the importance of Retrieval-Augmented Generation (RAG) for accessing up-to-date information
- Learn how agents use RAG to improve reasoning and decision-making
- Explore orchestrator agents, which coordinate multiple specialized agents to complete complex workflows
- Get introduced to the basics of multi-agent systems and how they communicate and collaborate
- Examine the potential risks and challenges of deploying agentic AI in real-world environments
Course prerequisites

Language
- English
Topic
- Artificial Intelligence
Skills You Will Learn
- Artificial Intelligence, Agentic AI, AI Agent, Generative AI, LLM
Offered By
- IBMSkillsNetwork
Estimated Effort
- 3 hours
Platform
- SkillsNetwork
Last Update
- July 15, 2025
Instructors
Wojciech "Victor" Fulmyk
Data Scientist at IBM
As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.
Read moreKaran Goswami
Data Scientist
I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.
Read moreFaranak Heidari
Data Scientist at IBM
Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.
Read moreTenzin Migmar
Data Scientist
Hi, I'm Tenzin. I'm a data scientist intern at IBM interested in applying machine learning to solve difficult problems. Prior to joining IBM, I worked as a research assistant on projects exploring perspectivism and personalization within large language models. In my free time, I enjoy recreational programming and learning to cook new recipes.
Read moreAbdul Fatir
Data Scientist
Abdul specializes in Data Science, Machine Learning, and AI. He has deep expertise in understanding how the latest technologies work, and their applications. Feel free to contact him with questions about this project or any other AI/ML topics.
Read more