Agentic AI Course
Design, build, and ship autonomous AI agents across no-code platforms, Python, and multi-agent systems
292 Hours
3 Months
06 Sept
Gurgaon
15 Mar
Interactive Live Online
08 August
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 tool it needs, and adjusts when something fails. It knows when to act, and how.
This Agentic AI course is the answer to 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 professionals available is 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 mentioned how there are more open agentic AI roles 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
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?
This course is specifically designed for:
- AI and Deep Learning career aspirants: If you are a fresher or switching into AI, you build a complete, job-ready skill set spanning generative AI, agentic AI systems, computer vision, and reinforcement learning, and you leave with multiple capstone projects to show in interviews and on your portfolio.
- Working professionals and data practitioners: Move from theory to production through hands-on Python projects covering LLM development and fine-tuning, agentic application development with LangChain, LangGraph, and CrewAI, RAG pipelines, and real-world deployment on FastAPI, Streamlit, and cloud platforms.
- Researchers and enthusiasts: Go deep on current agentic AI, including multi-agent orchestration with CrewAI, the Model Context Protocol (MCP), multimodal LLMs, transformer architectures built from scratch, and reinforcement learning techniques such as DQN, DDPG, and DPO.
Job Roles
- GenAI Associate / AI Analyst (Junior)
- Prompt Engineer (Entry-level)
- No-Code Automation Specialist
- AI Operations Executive
- Digital Transformation Associate
- AI Product Manager (GenAI focus)
- AI Research Engineer
- AI Solutions Architect (GenAI)
- AI Platform Engineer
- Conversational AI Engineer
- RAG Architect
- Agentic AI Engineer
- GenAI Developer
- LLM Application Developer
- NoCode AI Builder
- AI Automation Manager
- AI Workflow Consultant
- AI Engineer (LLM / GenAI)
Programming Tools and Languages
Agentic AI Course Delivery Format
Fully Interactive Live Online
Blend the dynamic experience of traditional classroom with engaging, real-time interactive sessions, carefully tailored to meet the demand of busy schedules. This innovative approach ensures effective learning, fostering a deeper understanding and retention of knowledge.
Blended with eLearning
Fuse the rich atmosphere of classroom instruction with the flexibility and accessibility of eLearning modules, meticulously integrated to accommodate learning preferences. This unique blend ensures an optimal learning experience, empowering participants to delve into subjects deeply.
Agentic AI Certification Details
On successful completion of all stipulated requirements, including a final evaluation, you will be awarded AnalytixLabs Certification. AnalytixLabs certifications are highly regarded in the industry due to our extensive domain expertise. As India’s leading Data Science and AI institute, we maintain rigorous integrity in our certification process.
To secure certification, you must meet the specified requirements within your course timeline. This includes:
- Completing all required case studies
- Passing multiple-choice assessments (MCQs)
- Fulfilling additional course-specific criteria
You will have two attempts to pass each assessment.
Certification must be secured within one year of your course registration. We provide essential hands-on experience throughout your learning journey to prepare you for industry challenges.
Agentic AI Course Fees & Learning Mode
Fully Interactive Live Online
₹ 42,000 + taxes
Blend the dynamic experience of traditional classroom with engaging, real-time interactive sessions, carefully tailored to meet the demand of busy schedules. This innovative approach ensures effective learning, fostering a deeper understanding and retention of knowledge.
- Fees payable in installments
- 0% Interest EMI – Pay in Easy Installments (though education financing partners)
- Cost-effective courses with high ROI, making it worth every penny you invest.
Admission Process at AnalytixLabs
We implement a personalized enrollment process for all our courses. To enroll in our AI courses, you must express your interest through our website, email, chat, or phone. Once we receive your interest in enrolling in our artificial intelligence certification course, one of our learning advisors will contact you within 24 to 72 hours.
Our learning advisor will help you understand the industry relevance of our course and assess whether this specific course is the best fit for your profile. If our advisor determines that your educational and professional background may be better suited for other classes, they will guide you to alternative options. Additionally, our advisor will assess your preferred location, upcoming course batches, and learning preferences.
We aim to ensure that you derive the maximum benefit from our courses, which requires us to understand your goals and expectations from our learning materials. Our learning advisors will provide comprehensive guidance on course details and job prospects and evaluate both your technical and non-technical knowledge, location, and preferred learning methods to tailor the best solution for you.
By paying the admission fee, as agreed upon with your learning advisor, you can secure your spot in the course at your chosen location and upcoming batch date.
Career Support and Guidance
AnalytixLabs is committed to helping you launch a successful career in AI, Data Science, and Analytics. With comprehensive Placement Assistance integrated into our Advanced Certification programs, our dedicated team of industry professionals offers personalized support based on your educational background and work experience. The placement process is designed to equip you with both opportunity and readiness—helping you transition seamlessly into the data-driven workforce.
Placement Guidance
Our training focuses on building job-ready skills that prepare students for successful placements.
Students receive continuous guidance from our experienced faculty on job applications and interview preparation. We also conduct mock interviews, when required, and provide one-to-one feedback and personalized guidance.
We also share placement opportunities, career guidance, and industry insights to keep students informed about current hiring trends.
Diverse Job Opportunities
• Access to job opportunities through direct collaborations with organizations, reputed recruiters, and the extensive AnalytixLabs Alumni network.
• Many students receive multiple interview calls and see a significant boost in career prospects.
• Success, however, depends on a combination of the candidate’s dedication and the institute’s support. Active participation and timely course completion are essential for maximizing placement outcomes.
Continued Career Support
Your journey with AnalytixLabs doesn’t end after completing the Placement Readiness Program. We offer ongoing placement support to ensure you stay on track until you achieve your career goals.
Our dedicated team continues to assist with job opportunities, interview preparation, and personalized guidance as long as you need it. Many of our students benefit from multiple interview calls and long-term career growth, thanks to the strong foundation and in-demand skills built during the course.
How To Apply
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What Students Say About Us?
True Stories, Transformative Career Experience
Piyush Ganar, Class of 2012 IIM Ahmedabad
(Director of Operations, Kenty.AI)
The course material is very easy to understand and the case studies were based on real time business problems. What I love the most about Sumeet and his team is that they never operated the institute like a typical commercial enterprise but more like a temple for learning. The gates of Alabs are always open for students for any kind of help and guidance. I would recommend ALabs to all.
Raajeev Kumar Sahu
(Senior Manager – Data Science, FinTech Startup)
I am from Advance Big Data Science course of Nov 2016 batch. The course was very structured and got real world problems to practice during the final case studies. It has also boosted my skills to start participating in the hackathons. Chandra sir was really helped me in preparing my resume right from very beginning of the course. Also, the placement team was very well organized and connected to industry leaders so that people get the right opportunity right after completing the course. I really recommend this institute, who are interested to transition into analytics field.
Sumit Asthana
(Assistant Manager, Senior Data Scientist)
The rapid pace at which the institutes are mushrooming all over India and Facebook newsfeed being inundated with thousands of options for analytics training, there are only handful of training institutes or rather say only couple of places where they prepare you to foray into rapidly growing analytics vertical. I have been working into legacy systems for past 5 years and was quite apprehensive if I will ever break into data science successfully.
Surbhi Sultania
(Analytics Manager, Mastercard Data & Services)
I took three months Big Data training from Analytix labs. Before joining this course, I had so many questions and doubts that will this course be worthy enough but later I found myself lucky to join this program. All trainers are amazing and always ready to help. Live examples and Case Studies are given which helps in understanding the concept and hands on practice.Not only they help in quality in depth knowledge transfer but also tend to give right direction to your career.
Vibhu Gautam
(Professor of Practice – Computer Science, UPES)
I had been associated with AnalytixLabs since 2012. This has been possible because of the mentor ship and support you get from faculty there specially from Sumeet and Chandra.I did a Business Analytic course with a focus on Marketing Analytic. The course deliver had been great and most importantly you never feel like you are part of formal classroom teaching. You are mentored there at every step, from Code Errors to Understanding Statistics behind it.
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Frequently Asked Questions
How to start learning agentic AI?
Our Agentic AI course is a good starting point for novices and experts. The learning roadmap consists of three main steps –
- Understand where Agentic AI fits in the whole AI universe
- Discover the capabilities of agentic AI and how it is impacting industries alike
- Learn the technical stack to build AI agents and integrate them in businesses to deliver insights that are tied to business goals
Learning the technical stack is essential if you want to move into technical roles. Various non-technical operational roles now rely on agentic AI to deliver outcomes. Roles such as program management, content delivery, stakeholder management, and HR operations require using no-code AI agents as assistants, allowing you to focus on strategies and streamlining.
Start with our agentic AI course and discover your true calling. Talk to our learning experts and see which role is ideal for you and how our course can help you achieve that.
Is agentic AI free?
Agentic AI is the concept or technology that drives various AI models, as well as no-code and low-code tools. Some tools are free while others have freemium or paid subscription models. In fact, certain aspects of agentic AI are free. For instance, open-source LLMs are free to use. You can use these for AI agent development and more. Similarly, various agentic AI tools and platforms are currently free to use.
Which course is best for AI?
“Best” can be subjective. Our Agentic AI course is often tagged as an autonomous AI system course, delivered by leading industry experts who bring practical experience, tactical understanding, and a futuristic approach to the learning modules. We’d say this Agentic AI course is one of the best AI courses to start with for AI. The course modules cover all the basic concepts of AI, genAI, and agentic AI, giving you a holistic learning experience. Explore our other popular artificial intelligence courses for a complete learning experience.
What are the best tools for building AI agents?
This course covers the following tools, which are ideal for building AI agents:
- OpenAI Suite
- ChatGPT (GPT-3.5, GPT-4, GPT-4-turbo)
- OpenAI API & Playground
- Token management tools
- Large Language Models (LLMs)
- Hugging Face Transformers
- Google Gemini / Bard
- Anthropic Claude
- Meta LLaMA
- LangChain
- Framework for building AI agents with dynamic workflows
- Integration & Automation Tools
- API & Workflow Automation
- Zapier / Make (Integromat)
- Slack API / WhatsApp Business API
- Telegram Bot API
- Google Sheets API / Airtable
- Low-Code AI Agent Builders
- Voiceflow (for conversational AI)
- Bubble (for custom UI)
- Retool (for internal tools)
This course will earn you an AI workflow automation certificate by combining these with practical use cases of multi-agent AI programming.
What is the agentic approach to AI?
By definition, the agentic approach to AI focuses on building systems that can autonomously perform tasks and self-learn from their surroundings and past performances. These autonomous AI systems can make informed decisions and interact with their surroundings to achieve specific goals, requiring minimal human intervention.
This Agentic AI course will teach you how to build and deploy such systems to achieve specific business goals.
What is Agentic AI?
Agentic AI are multi-agent AI systems that can act autonomously, make decisions, and take actions without requiring any human intervention. These systems utilize large language models (LLMs) to understand the context and adapt accordingly to their environments.
To learn more about Agentic AI, read our article on What is Agentic AI.
How does Agentic AI differ from traditional AI?
Artificial Intelligence (AI) is a broad field of computer science that focuses on creating machines capable of performing tasks that would otherwise require human intelligence. It includes functions such as learning, problem-solving, and decision-making, as well as technologies like machine learning, natural language processing (NLP), and computer vision.
Agentic AI, on the other hand, is a specific type of AI that focuses on creating machines that can function autonomously, adapt to their environments, and make informed decisions.
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