Applied Generative AI and Agentic AI
Master in-demand AI skills with a Generative AI and Agentic AI course that accelerates your career.
Course Overview
Agentic AI now sits at the core of most business functions. An agent decides the next step, calls the right tool, and adjusts when something fails. It knows when to act, and how.
This Agentic AI course addresses the widening gap between available jobs and skilled professionals. The global job market is seeing steady growth in new agentic AI roles, but the number of skilled talent available has yet to match the pace. Learn Agentic AI skills that matter, because opportunities will only grow manifold.
Most organizations have people who can write prompts but very few have people who can put an agent into production and keep it stable when it fails. This gap shows up in hiring too. In our latest talent report, we noted that more agentic AI roles are open than qualified candidates.
Two things follow from that shortage.
- Experienced practitioners command a higher salary band because supply is thin.
- Professionals with 2 to 3 years in adjacent roles can enter on demonstrated skill, not years served. That rarely happens in an established field.
Close that gap by building an agent, deploying it, and keeping it stable in production. This Agentic AI online course is the right starting point.
This is why most hiring managers frame interview questions around handling an autonomous agent in production. They ask what you did when a tool call returned nothing and the agent kept looping or how you capped the token spend on a workflow that ran unsupervised overnight.
This Agentic AI certification course will teach you where, why, and how to place a human checkpoint with your autonomous agents. You see what happens when you leave that checkpoint out. You then debug the same class of failure yourself, across both modules.
What You Will Learn?
In this course, you learn how AI agents work across industries and domains. You build and deploy them yourself. You then create multi-agent systems, streamline the workflows around them, and tie agent development to business goals.
This course builds six core skills. You apply each one in a working build before the programme ends.
Agent architecture: Understand memory, planning, tool use, and autonomy as separate components you can debug individually.
Framework fluency: Work hands-on in LangChain, LangGraph, CrewAI, and AutoGen. You learn when each one fits and when it does not.
No-code and low-code automation: Build production workflows in Zapier and Make, then extend them in n8n. Python comes after that.
Retrieval and grounding: Design RAG pipelines with vector databases so your agents answer from your data, not from guesswork.
Deployment: Ship agents through FastAPI, Streamlit, and Gradio, then monitor them with LangSmith.
Responsible AI: Apply guardrails, human-in-the-loop checks, and output verification to systems that act on their own.
What Are AI Agents? Types and Examples Covered in This Course
An AI agent perceives its environment, decides what to do next, and acts. The decision loop is what separates an agent from a chatbot that only responds.
Agent design has a long history, and the categories still shape how you build today.
Types of AI Agents
Simple reflex agents: Respond to the current input and nothing else. A thermostat is the textbook case.
Model-based agents: Hold an internal picture of the world. They can act sensibly when they cannot see everything at once.
Goal-based agents: Plan a sequence of steps toward an outcome. Route planning works this way.
Utility-based agents: Weigh competing options and pick the one with the best expected payoff.
Learning agents: Improve from feedback rather than staying fixed after deployment.
Knowledge-based agents in AI: Reason over a stored knowledge base before acting. They separate what they know from how they reason about it. This matters in practice. A RAG pipeline is a modern knowledge-based agent.
AI Agents Examples
Research agent: A research agent takes a question, searches multiple sources, and returns a cited summary.
Support triage agent: A support triage agent reads an incoming ticket, classifies it, and routes it to the right queue.
Ops reconciliation agent: An ops reconciliation agent compares a payment file against invoice records each morning.
Course Curriculum
This Agentic AI course offers a solid understanding of where and how Agentic AI fits in the AI world. With hands-on projects and assignments, you will transition smoothly into the core technologies.
The program also includes additional modules that extend your learning.
Download the brochure for the complete module-wise curriculum.
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Key Skills
Build the capability to
design, deploy and lead AI systems
Build
Autonomous AI agents, completing multi-step tasks end-to-end without human intervention at every step.
Design
Multi-agent orchestration systems that coordinate specialized agents across complex workflows.
Deploy
Production-ready GenAI and Agentic AI applications using RAG pipelines, LLMOps practices and responsible AI frameworks.
Automate
Complex operational workflows using Agentic AI, moving beyond prompting and into systems that reason, decide and act across enterprise functions.
Lead
AI initiatives inside your organisation from identifying high-impact opportunities to pitching, implementing and governing AI systems at scale.
Advance
Your career trajectory into AI-native roles or as the AI-capable leader in your current domain.
Professionals from leading organizations trust this Generative AI and Agentic AI course to advance their careers
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Questions About Our
GenAI & Agentic AI Course
Find answers to common questions about our Generative AI and Agentic AI program, practical frameworks, multi-agent systems, deployment, and how it accelerates your tech career.
Generative AI creates content, while Agentic AI takes autonomous actions, uses tools, and completes multi-step workflows. Learning these skills puts you at the core of modern tech development where open job roles far outnumber qualified candidates.
The program covers both no-code/low-code automation tools (Zapier, Make, n8n) and advanced Python-based agent frameworks, making it adaptable whether you are starting fresh or expanding your technical expertise.
You will work hands-on with leading frameworks like LangChain, LangGraph, CrewAI, and AutoGen, alongside deployment tools such as FastAPI, Streamlit, Gradio, and LangSmith for monitoring.
Yes! Every core skill includes working builds—from designing autonomous agents and RAG pipelines with vector databases to deploying production-ready multi-agent workflows.
Hiring managers specifically look for engineers who can handle autonomous agents in production, debug failures, and manage token costs. This course trains you directly on those real-world production challenges.