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Agentic AI Course

Agentic AI Course – Best Online Training & Certification

Get your hands on our Agentic AI Certification Course and learn skills that help you build LLM agents which can think, plan and act on their own. From exploring how agents are structured, to how they connect with tools and API.

Duration: 12 - 14 Weeks | Mode: Live + Recorded Sessions

Check Agentic AI Course Demo Videos

Check our Agentic AI Course to experience real-time training before you enrol

Our Recently Placed Students

Rohit Shah

Placed at Capgemini

Ankit Mishra

Placed at Microsoft

Gopal Singh

Placed at Infosys

Pawan Kumar

Placed at IBM

Vinod Sharma

Placed at Wipro

Manoj Prasad

Placed at Deloitte

Suman Sharma

Placed at TCS

Gita Singh

Placed at Accenture

About the Agentic AI Training

Our Agentic AI Training is built to help you learn how to create, test, and launch smart AI agents using Large Language Models (LLMs). Through this Agentic AI Certification Course, you will learn the exact skills needed to build agents that solve real problems in real work environments. The program is practical, easy to follow, and part of our complete Agentic AI Certification Training designed for beginners and working professionals.

Training Highlights
  • Live instructor-led classes where you can ask questions anytime
  • Hands-on Cloud Lab access on platforms like Google Cloud and AWS
  • Teaching that supports certification goals, including Agentic AI Online Course exams
  • Practical case studies and real-time projects like building an autonomous QA agent
  • Updated learning material covering tools like LlamaIndex and LangChain
  • Resume building and interview help for AI and ML roles
  • Placement support after completing the Agentic AI Online Training

What You Get

  • Live classes with access to all recordings
  • Cloud environment access to practice lab tasks
  • Real-time project work, including multi-agent systems
  • Interview and resume support to help you get job-ready

Course Design & Approved By

Nasscom & Wipro

What You Will Learn in Agentic AI Course?

Our Agentic AI Online Training gives you clear, step-by-step guidance on how to build LLM-powered agents from start to finish. This Agentic AI Online Course is made for anyone who wants to understand every core part of Agent development in a simple and practical way.

Core Modules Covered

  • Introduction to LLM Agents
  • Prompt Engineering for Agents
  • Agent Tool Use (function calling)
  • Memory Management for Agents
  • Planning & Reasoning
  • RAG for Agents
  • Ethical AI and Agent Safety

Advanced Topics & Projects

  • Multi-Agent Systems
  • Agent Frameworks like LangChain
  • Agent Evaluation & Benchmarking
  • Agent Deployment
  • This Agentic AI Course helps you get job

Download Curriculum

Get a peek through the entire curriculum designed that ensures Placement Guidance

Course Design By

nasco wp

Course Offered By

Why Choose Our Agentic AI Training Material & Resources?

  • Training videos and LLM agent
  • Module-wise notes and Colab Notebook
  • Real-time agent development tasks
  • Practice exercises
  • Certification-focused questions

Benefits of Joining Our Agentic AI Course

  • Learn directly from certified expert
  • Access to cloud environments
  • Lifetime LMS access
  • Recorded videos available anytime
  • Regular doubt-clearing sessions
  • Support for certification and job
  • 100% placement assistance
Learners Reviews

“The placement support and doubt sessions were great. This training helped me move from basic Python knowledge to building working AI agents.”

— Vijay, Autonomous Systems Developer

“Amazing experience! The trainers explained everything patiently, and the projects gave me real confidence to work on Agentic AI tasks.”

— Allen Joseph, Fresher

“The Agentic AI Course material was updated and useful. I finally understood LLM agents, tool use, and planning without feeling confused.”

— Amit Kulkarni, Senior Data Analyst

“I gained real hands-on skills. The step-by-step teaching and interview help really prepared me for AI and ML job roles.”

— Soniya Verma, AI Aspirant

“The Agentic AI Training was practical and clear. Cloud labs, demos, and friendly instructors made learning Agentic AI Training much easier.”

— Dev Singh, Prompt Engineer

“This Agentic AI Course made complex topics simple. The live sessions, projects & support helped me feel confident building real AI agents.”

— Aniket Sharma, LLM Engineer
Agentic AI - Country-wise Job Profiles & Salary Guide

Top Job Profiles:

  • LLM Engineer
  • Generative AI Developer
  • AI Research Scientist (Agents)
  • Prompt Engineer
  • Data Scientist (AI/ML)

Average Salary Range:

  • INR 6 LPA - INR 12 LPA (Entry Level)
  • INR 15 LPA - INR 30 LPA (Mid Level)
  • INR 30 LPA - INR 60+ LPA (Senior Level)

Top Job Profiles:

  • AI Engineer – LLM Agents
  • Generative AI Specialist
  • Machine Learning Scientist
  • Autonomous Systems Developer
  • AI/ML Platform Engineer

Average Salary Range:

  • $100,000 - $140,000 (Entry Level)
  • $150,000 - $200,000 (Mid Level)
  • $220,000 - $300,000+ (Senior Level)

Top Job Profiles:

  • Generative AI Engineer
  • LLM Agent Systems Developer
  • AI/ML Consultant
  • Advanced Data Scientist

Average Salary Range:

  • £45,000 - £65,000 (Entry Level)
  • £65,000 - £90,000 (Mid Level)
  • £90,000 - £120,000+ (Senior Level)

Top Job Profiles:

  • AI/ML Engineer – Agents
  • Software Developer (LLM Focus)
  • Data Scientist – Generative AI
  • AI Innovation Consultant

Average Salary Range:

  • CAD 80,000 - CAD 110,000 (Entry Level)
  • CAD 110,000 - CAD 150,000 (Mid Level)
  • CAD 150,000 - CAD 200,000+ (Senior)

Enroll Today

Take the leap into the future with our Agentic AI Course. Join our Agentic AI Online Training to understand how modern autonomous systems work & build intelligent agents yourself.

About the Trainer

Get hands-on training from a certified Agentic Ai Course expert who has 10+ years of real-world work experience in AI/ML, Large Language Models, Generative AI, and autonomous agent development.

  • Extensive hands-on experience in AI/ML with over 10 years.
  • Expertise in LLM architectures, prompting, and tooling.
  • Conducted more than 120 corporate and online AI training batches.
  • Live Demos of Real-World Agent Deployment
  • Step-by-step guidance in cloud labs
  • Complete support in certification and interview preparation
Frequently Asked Questions

Agentic AI Course is ideal for developers, data scientists, AI fans or anyone interested in creating Agentic AI agents and using LLMs.

Certainly! You will engage with projects, practical coding tasks and cloud laboratories to create agents that address real-world challenges.

You will gain experience using well-known tools and frameworks such as LangChain, LlamaIndex, AutoGen and more to create, evaluate and launch intelligent agents.

It equips you for sought-after AI/ML positions. You will acquire expertise in LLM agent creation, agent frameworks, agent implementation and receive support, for certification test preparation.

Absolutely! The course begins with concepts of LLM agents and gradually progresses to more complex subjects making it accessible, for beginners as well.

Yes. You will receive doubt-solving sessions, guidance from mentors and assistance, with projects to guarantee you always stay on track during your learning journey.

Some Python knowledge is recommended. The course explains coding step by step as you build AI agents, making it beginner-friendly.

CURRICULUM & PROJECTS

Agentic AI Training Program

    Agentic AI Introduction

    AI Agents vs. Agentic AI

    Comparison: Agentic AI, Generative AI, and Traditional AI

    Agentic AI Building Blocks

    Autonomous Agents

    Human in the Loops Systems

    Single and Multi Agent AI Systems

    Agentic AI Frameworks Overview

    Ethical and Responsible AI

    Agentic AI Best Practices

    AI Implementation Success Stories: Case Studies

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    Agentic AI Architecture

    Agentic Architecture Types

    Key Components of the Agentic AI Framework

    Perception Module

    Cognitive Module

    Action Module

    Learning Module

    Collaboration Module

    Security Module

    Agentic AI Design Patterns

    Reflection Pattern

    Tool Use Pattern

    Planning Pattern

    ReAct (Reasoning and Acting) and ReWOO (Reasoning with Open Ontology)

    Multi Agent Pattern

    Design Considerations

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    Components and Modules

    Data Ingestion and Document Loaders

    Text Splitting

    Embeddings

    Integration with Vector Databases

    Introduction to Langchain Expression Language (LCEL)

    Runnables

    Chains

    Building and Deploying with LCEL

    Deployment with Langserve

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    Introduction to LangGraph

    State and Memory

    State Schema

    State Reducer

    Multiple Schemas

    Trim and Filter Messages

    Memory and External Memory

    UX and Human-in-the-Loop (HITL)

    Building Agent with LangGraph

    Long Term Memory

    Short vs. Long Term Memory

    Memory Schema

    Deployment

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    What is Agentic RAG

    Agentic RAG vs. Traditional RAG

    Agentic RAG Architecture and Components

    Understanding Adaptive RAG

    Variants of Agentic RAG

    Applications of Agentic RAG

    Agentic RAG with Llamaindex

    Agentic RAG with Cohere

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    Agents

    Models

    Tools

    Knowledge

    Chunking

    Vector DB

    Storage

    Embeddings

    Workflows

    Developing Agents with Phidata

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    Multi Agent Systems

    Multi Agent Workflows

    Collaborative Multi Agents

    Multi Agent Designs

    Multi Agent Workflow with LangGraph

    CrewAI Introduction

    CrewAI Components

    Setting up CrewAI environment

    Building Agents with CrewAI

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    Autogen Introduction

    Salient Features

    Roles and Conversations

    Chat Terminations

    Human-in-the-Loop

    Code Executor

    Tool Use

    Conversation Patterns

    Developing Autogen-powered Agents

    Deployment and Monitoring

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    Langfuse Overview

    Langfuse Dashboard

    Tracing

    Evaluation

    Managing Prompts

    Experimentation

    AI Observability with Langsmith

    Setting up Langsmith

    Managing Workflows with Langsmith

    AgentOps Practical Implementation

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    Introduction to No-Code/Low-Code AI

    Benefits and Challenges of No-Code AI Development

    Key Components of No-Code AI Platforms

    Building AI Workflows Without Coding

    Designing AI Agents with Drag-and-Drop Interfaces

    Integrating No-Code AI with Existing Systems

    Customizing and Fine-Tuning AI Solutions

    Optimizing Performance and Efficiency in No-Code AI

    Security and Compliance Considerations in No-Code AI

    Best Practices for Deploying No-Code AI Solutions

    Real-World Use Cases and Applications of No-Code AI

    Scaling and Future Trends in No-Code AI

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+ More Lessons

Course Design By

naswipro

Nasscom & Wipro

Course Offered By

croma-orange

Croma Campus

Our Students' Projects
1768384877.webp
Zerodha – Financial Trading Assistant Agent

Scenario: A FinTech company wanted an agent to provide real-time investment insights and suggest trade actions based on live market fluctuations.

Live Work:
  • Made a tool to fetch live stock data
  • Built a module to analyze investment strategies
  • Set up the agent to give trade suggestions
  • Shows all recommendations in a simple

Outcome: Provided immediate, Improving decision

1768384689.webp
DXC Technology– Agentic Workflow

Scenario: A legal or financial services firm needed to accurately extract specific entities and structured data from various unstructured document.

Live Work:
  • Made a parsing agent
  • Used Pydantic to organize the extracted data
  • Added a verification agent to check data against
  • Made sure the system gives accurate result

Outcome: Achieved high precision in data extraction.

1768384465.webp
Google – LLM Agent Deployment Pipeline

Scenario: A cloud service provider sought to build a robust CI/CD pipeline to deploy new or updated AI agents as scalable, low-latency API endpoints, essential.

Live Work:
  • Used Docker to run the agent in a stable
  • Automated deployment with Kubernetes
  • Added monitoring and logging to keep an eye
  • Made it easy to roll back quickly

Outcome: Reduced agent deployment time.

1768384305.webp
Cognizant– Agent Evaluation Dashboard

Scenario: A Machine Learning Operations (MLOps) team needed an automated dashboard to track the performance.

Live Work:
  • Made a system to record and check agent outputs
  • Kept track of which tools agents used successfully
  • Created a dashboard to show speed, errors
  • Added cost tracking to see

Outcome: Enabled data-driven tuning and optimization.

1768384173.webp
Shopify– Customer Support Chatbot (RAG Agent)

Scenario: A popular e-commerce platform required a sophisticated chatbot that uses internal knowledge bases (RAG) to resolve queries.

Live Work:
  • Set up a system to fetch answers
  • Made an Escalation Agent that passes tricky
  • Improved the way questions are asked
  • Made sure the system gives fast

Outcome: Increased automated ticket resolution by 15%

1768383999.webp
Ericsson – Real-Time Network Debugging Agent

Scenario: A software development team needed an autonomous assistant to handle simple programming tasks, from writing the code to fixing errors.

Live Work:
  • Linked the agent to a tool that can run
  • Created a Debugging Agent that finds errors
  • Focused on writing Python code for real tasks
  • Worked on automating code deployment.

Outcome: Reduced time spent by developers.

1768383799.webp
IBM – Multi-Agent Quality Assurance System

Scenario: A tech company required a highly reliable Q&A system for internal technical documentation that ensures accuracy through an automated peer-review process.

Live Work:
  • Created a small team of agents: Researcher, Critic
  • The Researcher collects and organizes
  • The Critic checks the work and suggests
  • The Editor gives the final approval

Outcome: Achieved near-human accuracy in answering

1768383433.webp
Deloitte – Autonomous Research Agent

Scenario: A global consulting firm needed an agent to accelerate market analysis by autonomously researching, summarizing, and synthesizing complex reports.

Live Work:
  • Used Tree-of-Thought reasoning to break complex
  • Created a Web Search tool to collect useful
  • Built a PDF analysis tool to read documents
  • Developed a final synthesis module that combines

Outcome: Reduced time spent on initial market analysis.

Recent Job Opportunities for Agentic AI Course Professionals
Prompt Engineer

Company: Amazon Web Services (AWS)

Location: Bangalore

Experience: 0–2 Years

Required Skills: ReAct, CoT, Custom Tool Development.

Generative AI Engineer

Company: Microsoft AI

Location: Hyderabad

Experience: 0–2 Years

Required Skills: LLMs, RAG, Prompt Engineering, Deployment on Azure.

LLM Agent Developer

Company: Google DeepMind

Location: Seattle / Remote

Experience: 1–3 Years

Required Skills: Python, LangChain/AutoGen, Tool Integration.

Who Can Take This Agentic AI Course?
  • Backgrounds : B.Tech, MCA, M.Sc (CS/AI), or graduates with Python skills
  • Why: Learn modern AI development from scratch and prepare for high-demand LLM/AI roles
  • Best Fit Modules: Foundations, Tool Use, RAG, Real-Time Projects
  • Why : Transition into Generative AI or LLM engineering roles
  • Best Fit Modules: Multi-Agent Systems, Tool Integration, Deployment & Scalability
  • Career Advantage: Use existing coding and system design knowledge to build advanced agents
  • Why : Build intelligent autonomous LLM applications beyond traditional ML models
  • Best Fit Modules: Advanced Planning, Evaluation & Benchmarking, Agent Integration
  • Career Advantage: Gain deep insights into model behavior and design robust agents
  • Why : Understand technical AI capabilities and deployment requirements
  • Best Fit Modules: Foundations, Multi-Agent Systems, Deployment Strategies
  • Career Advantage: Bridge the gap between business needs and technical AI implementation
  • Why : Professionals from other tech fields like Web Development or Data Analytics can shift to Generative AI
  • Best Fit Modules: All modules covering core concepts, agent workflows, and real-time projects
Our Agentic AI Courses

Choose from our specialized AI modules designed for all learning levels.

Machine Learning Online Course

Enroll in the Machine Learning Online Course to learn Python, AI, ML algorithms, and real-time projects from anywhere.

Python with AI Course

The course will help you understand how Python relates to AI models, data handling and writing AI logic.

Artificial Intelligence Training

This course teaches concepts relating to AI in a beginner-friendly way that goes from basics to advanced levels.

Generative AI Online Course

This course elaborates on how Generative AI is applied in real-world projects using modern AI tools and frameworks.

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