Agentic AI Course
Design, build, and ship autonomous AI agents across no-code platforms, Python, and multi-agent systems
292 Hours
3 Months
27 Sept
Noida
15 Mar
Interactive Live Online
04 Oct
Interactive Live Online
29 Mar
Gurgaon
Learning Modes
Interactive Live Online sessions
Live, Interactive classes—Right on your screen, with instant doubt support.
Blended eLearning
Your Learning, Your Pace—backed by real-time interaction and doubts support.
Courses Included
Artificial Intelligence Engineering
₹ 20000/-
Artificial Intelligence Engineering
₹ 20000/-
Artificial Intelligence Engineering
₹ 20000/-
₹ 65000/-
₹ 48000*/-
Optional Course
Data Science using R
₹ 20,000
₹ 4000/-
04 Jan
Interactive Live Online
Interactive Live Online
INR 48,000 + Taxes
(0% Interest EMI – Pay in Easy Installments)
Overview
90 Hours Live Training
+ eLearning
Self Study & Practice
Projects & Assignments
Placement Support
and Career Guidance
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.
Why Learn Agentic AI?
Agentic, by definition, means the capacity to act independently and make one’s own choices. Transcending this meaning to agentic AI, these are autonomous AI systems that can act autonomously and make decisions independently without human intervention.
These systems can learn, implement, execute, and iterate tasks to achieve set outcomes independently. While traditional AI is rule-based and requires human monitoring, agentic AI is statistical, where it learn and adapts from its surroundings and historical data.
Agentic AI is leading the way in every industry and domain. Learning the concepts and workings of these autonomous agents will give you a competitive edge in a time when skilled AI professionals are in short supply compared to the total number of opportunities.
The demand-versus-skilled professional gap is one reason why companies are willing to pay skilled agentic AI professionals more than the average payout. There’s no better time to upskill or reskill than now.
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. The vector database stands in for the
classical knowledge base.
AI Agents Examples
- Research agent:
A research agent takes a question, searches multiple sources, and returns a
cited summary. It decides how many sources to check before it stops. - Support triage agent:
A support triage agent reads an incoming ticket, classifies it, and routes
it to the right queue. It pulls the order history first, then decides
whether to escalate or resolve automatically. - Ops reconciliation agent:
An ops reconciliation agent compares a payment file against invoice records
each morning. It flags mismatches, drafts the follow-up email, and holds
anything above a set value for human sign-off.
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. At the same time, you build the surrounding skills including critical thinking, AI ethics, communication, and AI project management.
The program also includes additional modules that extend your learning.
Download the brochure for the complete module-wise curriculum.
What is Artificial Intelligence (AI)?
Introduction to Course Objective - Logistics - Structure of the course
Demystifying AI and its evolution
Data Science vs. Machine Learning vs. Artificial Intelligence
How AI is transforming businesses
Trends, Tools and Applications
Use cases related to different functions across industries (Retail/ e-commerce, BFSI, Pharma, Manufacturing, Auto, etc.)
Key Areas of AI (Computer Vision, Language Models, Reinforcement Learning, etc.)
What is Generative AI
Understand how Generative AI produces new data, text, or images based on training datasets.
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Generative AI & Nocode Agentic AI
AI Foundations
- AI vs. Automation vs. Analytics
- AI, Machine Learning (ML), Deep Learning (DL) – differences & connections
- Narrow AI vs. General AI vs. Super AI
- Core AI applications (NLP, CV, Robotics, Predictive Analytics)
Generative AI Basics
- Traditional AI vs. Gen AI
- Every day, GenAI applications
- How does GenAI work?
- Key models (GPT, Claude, Gemini, LLaMA)
- Text generation & completion techniques
- Prompt Engineering basics: role, task, context
- Core Prompting Tasks (Text generation & completion, Summarization (short & detailed), Rewriting in different tones/styles)
- Prompt Structures (instructional, role-based)
- Prompting Techniques (Zero-shot prompting, Few-shot prompting, Chain of Thought (CoT) prompting
- Role Prompting, Multi-turn interactions (context retention), etc..
- Limitations of prompting
- Evaluating GenAI output quality
Large Language Models (LLMs): How They Work
- Introduction to transformers
- LLMs vs. SLMs
- Popular LLMs (GPT, Claude, Gemini, LLaMA) – Overview – Strengths & Limitations
- Introduction to Embeddings
Introduction to Agentic AI
- No-code AI overview
- Intro to workflow automation tools
- Introduction to Zapier basics (triggers & actions), n8n workflow design, Make.com integrations
- AI-powered productivity with Notion AI for task automation & summarization, Airtable for data storage & automation, Glide for mobile app prototyping, etc.
- Design & Storytelling Tools
- Integrating AI into everyday workflows – Linking AI workflows with business processes
- Prototyping with no-code/low-code tools
What is an AI Agent? How it differs from chatbots
- Agentic AI vs. AI Agents
- Key components of Agentic AI: memory, planning, tool use, autonomy
- AI Orchestration with no-code tools
- Agent frameworks (LangChain, AutoGen, CrewAI – overview)
AI challenges: Bias, hallucination, privacy & security issues
- Responsible AI principles: Fairness, Accountability, Transparency, Explainability
- Global AI Ethics Frameworks
- Safe & Responsible usage
- Responsible AI: Guardrails
Applied Agentic AI (Agentic AI Systems using Python)
Setting up the development environment
- Installing and configuring tools like VS CODE, JUPYTER LAB, GitHub
- Best practice for managing dependencies and optimizing the workspace
- Numpy & Pandas for AI workflows
- Functions & Classes in Python
- Working with APIs (REST APIs, JSON handling, requests library)
Transformers & Fine-Tuning
- Transformers Architecture Basics(Introduction, attention, multihead attention, encoder-decoder with context)
- The Main Idea behind the Transformer
- Coding self-attention in Pytorch
- Self-attention and Masked Self-attention
- Hugging Face Transformers basics
- Pre-trained vs fine-tuned models
- Fine-tuning pre-trained models (BERT, GPT-style)
- Parameter-efficient fine-tuning (PEFT, LoRA, adapters)
GPT from Scratch
- Language modeling concepts
- Tokenization & embeddings
- Implementing a simple GPT model step by step
- Training with a toy dataset
- Training loop & loss function
Multi-Modal LLMs
- Text-to-Image models (Stable Diffusion, DALL·E)
- Image-to-text models (BLIP, CLIP, Flamingo)
- Text-to-Speech and
- Speech-to-Text APIs
- Speech-to-text (Whisper)
- Combining modalities (vision + text)
- Multi-modal pipelines (text+image, text+audio)
LLM Prompting & LangChain Basics
- LLM Wrappers & APIs
- Prompt engineering strategies
- LangChain components: Memory, Tools, Chains, Agents
- LangChain wrappers and memory
- Orchestrating LLM workflows
- Building basic agents
Vector Databases & RAG (Retrieval-Augmented Generation)
- Introduction to embeddings (SBERT, OpenAI, Cohere)
- Vector embeddings and similarity search
- FAISS & Pinecone, Weaviate for vector search
- Chunking & indexing documents
- Building RAG(Retrieval-Augmented Generation) pipelines
- Integrating Fusion & Re-ranking (Practical Sketch)
Autonomous Agents (Agentic AI)
- Introduction to LLM Agent Systems
- CrewAI & AutoGen frameworks
- Multi-agent coordination
- Task decomposition & delegation
- Building workflow-driven AI agents
- Tools, memory, reasoning
- Deployment & Use Cases
LangGraph & Model Context Protocols (MCP)
- LangGraph introduction
- Designing agent workflows with graphs
- State management in multi-agent systems
- MCP for standardized context sharing
- Future of agentic AI ecosystems
- Agent to Agent Protocols (A2A)
Testing & Evaluating LLM Apps/outputs (metrics, hallucination checks & frameworks)
- Monitoring & debugging with LangSmith
- App deployment with Streamlit, Gradio
- API deployment with FastAPI
Why Learn No-code/Low-Code Automation Tools?
You do not need Python to build your first working agent. This course opens with no-code and low-code automation tools, so you understand workflow logic before you write code.
A no-code automation platform lets you connect apps and trigger actions through a visual builder. A low-code automation platform adds scripting where the visual builder runs out of room. Both matter, because most business automation lands somewhere between them.
You work hands-on with no-code automation tools including Zapier, Make, Notion AI, Airtable, and Glide. You then move to low-code automation tools such as n8n, where you add custom functions and API calls to a visual workflow.
Low-code automation also has a ceiling. You will find it during Module 1, when a workflow needs branching logic or memory that the builder cannot express. That is the moment Python earns its place, and it is where Module 2 begins.
Key Skills
- Understand the foundations of AI and Generative AI
- Master prompt engineering and prompting frameworks for real-world scenarios
- Automate workflows with no-code/low-code AI tools
- Design and deploy AI Agents for business and personal productivity
- Understand core AI & GenAI fundamentals
- Build LLM-powered apps with LangChain & Vector DBs
- Design & deploy AI agents using Python frameworks
- Work with practical enterprise use cases in automation
- Apply AI responsibly with awareness of ethical, bias, and governance considerations
Learning Outcomes
Graduates of the program will be able to:
- Communicate AI concepts to both technical and business audiences.
- Design prompts and no-code workflows for productivity and automation.
- Build and deploy LLM-based apps with LangChain, RAG, and multi-agent systems.
- Implement computer vision models for tasks like image classification, surveillance, or defect detection.
- Apply reinforcement learning for decision-making, optimization, and autonomous systems.
- Develop advanced AI prototypes — from chatbots to CV-enabled drones.
- Integrate AI safely and ethically into real-world workflows.
- Showcase an AI portfolio with capstone projects across Generative, Agentic, and Advanced AI.
Projects
- Objective: Build an AI assistant that helps with productivity using GenAI + No-Code Automation using tools like zappier, make or n8n etc..
- Problem Identification: Choose a real productivity pain point (e.g., too many emails, meeting overload, research notes etc..)
- Workflow Design: Build automation using Zapier/n8n to connect AI with apps (Notion, Google Docs, Slack).
- Chatbot Assistant: Use ChatGPT/Notion AI for contextual Question & answers.
- Interface Prototype: Build front-end in Glide/Tome AI.
- Final Presentation & Demo: Live demo + explanation of workflow + reflection on ethics
- Should automate at least 3 tasks (e.g., meeting notes → task assignment → daily summary email)
- End-to-end AI Agentic Workflow integrating multiple modules.
Assignments
- Add extra features: e.g., voice input, task prioritization, or reminders.
- Explore alternative integrations (Slack, WhatsApp bots).
- Document your workflow and share video demo
Who Should Do?
- Career changers moving into AI – You build a job-ready skill set across generative and agentic AI, and you leave with capstone projects you can defend in an interview.
- Data practitioners – You move from theory to production through Python projects covering LangChain, LangGraph, CrewAI, RAG pipelines, and deployment.
- Functional managers and team leads – You learn enough architecture to scope agent projects, judge vendor claims, and set realistic timelines.
- Researchers and technical enthusiasts – You go deep on multi-agent orchestration, MCP, multi-modal LLMs, and transformer internals.
