GenAI & Agentic AI
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GenAI & Agentic AICourse Overview
Artificial Intelligence is transforming how the world works, communicates, creates and makes decisions. Generative AI and Agentic AI represent the next major evolution in this transformation.
The GenAI & Agentic AI programme introduces learners to advanced AI technologies that can generate human-like text, images, code, reports and other digital content while also enabling intelligent agents to plan, reason, execute tasks and automate complex workflows.
Generative AI focuses on creating new content using advanced models such as Large Language Models (LLMs), while Agentic AI takes AI capabilities further by enabling systems to perform multi-step tasks, interact with tools, retrieve information and make decisions with appropriate human supervision.
Through a combination of conceptual learning, practical exercises, real-world projects and industry-oriented applications, learners develop the ability to design, build and deploy AI-powered solutions.
What Makes This Programme Special?
- Learn the fundamentals of Generative AI and Agentic AI.
- Explore Large Language Models and advanced AI applications.
- Build intelligent AI chatbots and virtual assistants.
- Develop autonomous AI agents and multi-agent systems.
- Learn prompt engineering and AI workflow automation.
- Work with real-world AI tools, frameworks and APIs.
- Create practical projects that demonstrate your technical abilities.
- Develop skills relevant to emerging AI-driven industries.
Skills
Develop practical, industry-relevant skills that help you move beyond simply using AI tools to actually building intelligent AI-powered applications.
Technical Skills
- Generative AI Fundamentals
- Agentic AI Architecture
- Prompt Engineering and Advanced Prompt Design
- Large Language Models (LLMs)
- Python Programming for AI
- Natural Language Processing (NLP)
- Retrieval-Augmented Generation (RAG)
- AI Chatbot Development
- AI Agent Development
- Multi-Agent System Design
- API Integration and Tool Calling
- Vector Databases and Semantic Search
- AI Workflow Automation
- Model Evaluation and Optimization
- Responsible AI and AI Security
Practical and Professional Skills
- AI-Based Problem Solving
- Critical Thinking and Logical Reasoning
- Workflow Designing and Automation
- Analytical and Decision-Making Skills
- Research and Innovation
- Project Development and Documentation
- Collaboration and Communication
- Business Process Optimization
Who can apply?
This programme is designed for learners who want to explore the future of Artificial Intelligence and build skills for technology-driven careers.
Eligible Candidates Include
- Students pursuing B.Tech, BCA, MCA, B.Sc, M.Sc, BBA, MBA or other undergraduate and postgraduate programmes.
- Computer Science and IT graduates interested in AI development.
- Software developers who want to integrate AI into their applications.
- Working professionals looking to upgrade their technical skills.
- Data analysts and business analysts interested in AI-powered solutions.
- Entrepreneurs and startup founders looking to automate business operations.
- Digital marketers interested in AI content generation and automation.
- Fresh graduates seeking opportunities in emerging technology domains.
- Technology enthusiasts passionate about innovation and intelligent systems.
Basic Requirements
- Interest in Artificial Intelligence and emerging technologies.
- Basic computer knowledge.
- Logical thinking and problem-solving interest.
- Basic programming knowledge is helpful for technical modules but may not be mandatory for beginner-level learning.
Job Roles
Generative AI and Agentic AI are creating new possibilities across software development, healthcare, finance, education, e-commerce, marketing, consulting and other industries.
After acquiring relevant technical skills and practical project experience, learners can explore opportunities such as:
Popular AI Career Roles
| Career Roles (1–9) | Career Roles (10–18) |
|---|---|
| 1. Generative AI Developer | 10. Conversational AI Developer |
| 2. Agentic AI Developer | 11. NLP Engineer |
| 3. AI Application Developer | 12. RAG Application Developer |
| 4. AI Engineer | 13. AI Chatbot Developer |
| 5. Machine Learning Engineer | 14. Multi-Agent Systems Developer |
| 6. Prompt Engineer | 15. AI Research Assistant |
| 7. LLM Application Developer | 16. AI Product Analyst |
| 8. AI Automation Engineer | 17. AI Consultant |
| 9. AI Solutions Architect | 18. Intelligent Automation Specialist |
Industries Hiring AI-Skilled Professionals
- IT and Software Development
- Banking and Financial Services
- Healthcare and Medical Technology
- E-commerce and Retail
- Education and EdTech
- Digital Marketing and Advertising
- Manufacturing and Automation
- Business Consulting
- Startups and Technology Companies
Syllabus/Curriculum
Our future-oriented curriculum covers the essential concepts, tools and technologies required to understand, develop and implement Generative AI and Agentic AI applications.
Module-wise Curriculum
| Module | Topics Covered |
|---|---|
| Module 1: Introduction to Artificial Intelligence | Evolution and fundamentals of Artificial Intelligence Types of AI and their applications Introduction to Machine Learning and Deep Learning Understanding Neural Networks AI applications across industries Future trends in Artificial Intelligence |
| Module 2: Python Programming for AI | Python fundamentals Variables, data types and operators Conditional statements and loops Functions and modules Object-oriented programming Working with libraries and APIs Data handling and processing |
| Module 3: Generative AI Fundamentals | Introduction to Generative AI How Generative AI models work Understanding generative models Introduction to Transformers Text, image and code generation Applications of Generative AI Benefits and limitations of GenAI |
| Module 4: Large Language Models (LLMs) | Introduction to Large Language Models LLM architecture and working principles Tokens, embeddings and context windows Understanding model parameters Introduction to GPT-based models Open-source and proprietary LLMs LLM integration and applications |
| Module 5: Prompt Engineering | Fundamentals of prompt engineering Zero-shot and few-shot prompting Role-based prompting Chain-of-thought prompting concepts Structured output generation Context management Prompt testing and optimization Advanced prompting techniques |
| Module 6: AI Application Development | Introduction to AI APIs Connecting applications with LLMs Building AI-powered applications Chatbot development AI-based content generation AI-assisted coding applications Application testing and deployment |
| Module 7: Retrieval-Augmented Generation (RAG) | Introduction to RAG architecture Document processing and chunking Text embeddings Vector databases Semantic search Knowledge retrieval Building RAG-based chatbots Improving response accuracy |
| Module 8: Introduction to Agentic AI | Understanding Agentic AI Difference between traditional AI, Generative AI and Agentic AI AI agent architecture Autonomous and semi-autonomous agents Reasoning and planning Task execution Memory and context management Human-in-the-loop systems |
| Module 9: AI Agent Development | Building AI agents Tool calling and function calling API integration Agent memory management Task decomposition Decision-making workflows Agent evaluation and debugging Building intelligent virtual assistants |
| Module 10: Multi-Agent Systems | Introduction to multi-agent architecture Agent collaboration and communication Role-based agent systems Task delegation Multi-agent orchestration Workflow coordination Building collaborative AI solutions |
| Module 11: AI Frameworks and Tools | LangChain fundamentals LangGraph fundamentals LlamaIndex fundamentals Introduction to AutoGen and CrewAI Hugging Face ecosystem OpenAI-compatible APIs Vector database integration AI application development environments |
| Module 12: AI Automation and Business Applications | Intelligent workflow automation AI-powered business processes Customer support automation Automated research assistants Document processing AI-powered data analysis Business productivity applications AI integration in enterprise systems |
| Module 13: AI Ethics, Security and Responsible AI | Responsible AI principles Data privacy and protection AI bias and fairness Prompt injection risks AI hallucinations Agent permissions and access control Human oversight and governance Secure AI application development |
| Module 14: Capstone Project and Industry Applications | AI-powered chatbot development Personal AI assistant Automated research agent RAG-based knowledge assistant Multi-agent business automation AI-powered content generation system Project testing, documentation and presentation |
Why choose it ?
Choosing the right technology programme can shape your professional future. This programme focuses on practical AI knowledge, modern development approaches and emerging industry applications.
- Future-Ready Technology: Learn technologies shaping the next generation of digital innovation.
- High-Demand Skill Development: Build capabilities relevant to AI-focused job opportunities.
- Practical Learning Approach: Understand AI concepts through hands-on exercises and projects.
- Industry-Relevant Curriculum: Explore tools and frameworks used in modern AI development.
- Learn by Building: Develop chatbots, AI assistants and intelligent automation systems.
- Multiple Career Pathways: Explore opportunities in AI engineering, software development, automation and consulting.
- Cross-Industry Applications: Understand how AI is transforming different business sectors.
- Portfolio Development: Create projects that showcase your practical knowledge.
- Beginner-Friendly Learning Path: Build foundational knowledge before progressing to advanced concepts.
Learning Module
The learning experience is designed to help students progress from basic AI concepts to advanced intelligent application development.
Learning Stage 1 – Build Your Foundation
Understand Artificial Intelligence and Machine Learning • Learn Python programming fundamentals • Explore the fundamentals of Generative AI • Understand how Large Language Models work
Learning Stage 2 – Master Generative AI
Learn advanced prompt engineering • Explore text and code generation • Integrate AI models through APIs • Build AI-powered applications and chatbots
Learning Stage 3 – Explore Intelligent AI Systems
Understand Agentic AI architecture • Develop intelligent AI agents • Learn tool calling and memory management • Explore autonomous task execution
Learning Stage 4 – Develop Advanced AI Solutions
Build RAG-based applications • Develop multi-agent systems • Implement AI workflow automation • Explore AI security and evaluation
Learning Stage 5 – Project-Based Learning
Work on real-world AI use cases • Develop individual or team-based projects • Create a portfolio of AI applications • Present and demonstrate your final project
Examination Pattern
The assessment structure is designed to evaluate both theoretical understanding and practical implementation skills.
Assessment Components
| Assessment Type | Purpose |
|---|---|
| Theory Assessment | Evaluate knowledge of AI concepts, architectures and technologies. |
| Practical Assignments | Apply concepts through coding exercises and AI tools. |
| Module-Based Tests | Assess understanding after completing individual modules. |
| Project Evaluation | Demonstrate the ability to develop AI-powered solutions. |
| Case Study Analysis | Explore real-world AI implementation scenarios. |
| Final Assessment | Evaluate overall programme learning and technical understanding. |
Suggested Assessment Structure
| Component | Weightage |
|---|---|
| Theory and conceptual understanding | 30% |
| Practical assignments | 25% |
| Projects and case studies | 35% |
| Final presentation or assessment | 10% |
Approvals
Students should always verify the academic and institutional recognition associated with the programme before applying.
Important Recognition Information
- UGC recognition applies to eligible universities and higher education institutions, not automatically to every short-term AI training programme.
- AI skill-development certificates should be distinguished from UGC-recognised academic degrees.
- Any degree-awarding institution must have the appropriate legal authority to award the advertised qualification.
- For online or distance degree programmes, verify the university and specific programme's applicable UGC-DEB recognition and approval for the relevant academic session.
- Professional certifications, industry training certificates and university qualifications have different recognition statuses.
Fee Structure
Invest in your future with a programme designed to help you develop valuable skills in emerging AI technologies.
Programme Fee Details
| Fee Component | Amount |
|---|---|
| Registration Fee | [Insert Amount] |
| Admission Fee | [Insert Amount] |
| Tuition Fee | [Insert Amount] |
| Examination Fee | [Insert Amount] |
| Study Material Fee | [Insert Amount, if applicable] |
| Certification Fee | [Insert Amount, if applicable] |
| Total Programme Fee | [Insert Total Amount] |
Flexible Payment Options
- One-time payment option, if available.
- Semester-wise payment option, if applicable.
- Instalment facilities, subject to institutional terms.
- Scholarships or eligible fee concessions, if available.
What Does Your Programme Fee Include?
- Structured learning modules
- Access to learning resources
- Practical assignments and exercises
- Project-based learning
- Assessment and evaluation
- Certification, subject to successful completion and programme terms
Take the First Step Towards Your AI Career!
Contact our admission counsellors to receive the latest fee details, available payment options and applicable scholarships.
Placement Assistance
Move closer to your career goals with career-oriented guidance and professional development support.
Our placement assistance approach aims to help learners prepare for opportunities in the growing Artificial Intelligence and technology sectors.
Placement Assistance Services May Include
Resume Building
Create a professional, ATS-friendly resume highlighting AI skills and projects.
Interview Preparation
Prepare for technical interviews and industry discussions.
Technical Skill Development
Strengthen your Python, GenAI, LLM and Agentic AI knowledge.
Portfolio Development
Showcase practical AI projects to potential employers.
Mock Interviews
Practise technical and HR interview questions.
Career Counselling
Identify suitable career pathways based on your skills and qualifications.
Job Opportunity Updates
Receive information about relevant job openings, where available.
Professional Development
Improve communication, presentation and workplace skills.
Industry Exposure
Participate in workshops, webinars and expert sessions, if offered.
Potential Career Pathways
AI Developer • Generative AI Engineer • Agentic AI Developer • AI Automation Specialist • LLM Application Developer • Machine Learning Engineer
Admission Process
Getting started with your GenAI & Agentic AI learning journey is simple and convenient.
- Explore the Programme Understand the curriculum, eligibility, learning outcomes, fee structure and certification details.
- Submit Your Enquiry Fill out the online enquiry form with your basic details to receive programme information.
- Connect with an Admission Counsellor Discuss your educational background, career interests and programme requirements.
- Check Eligibility Verify that you meet the admission requirements specified by the institution.
- Complete the Application Form Provide the necessary personal, academic and contact information.
- Upload Required Documents Submit the relevant documents for verification.
- Complete the Fee Payment Pay the applicable programme fee through the institution's authorised payment channel.
- Receive Admission Confirmation After successful verification and completion of admission formalities, receive your admission confirmation and further instructions.
- Begin Your Learning Journey Access the learning platform, study resources and programme schedule as applicable.
Required Documents
Applicants may be required to provide the following documents during the admission process, depending on the programme level and institutional requirements.
| Document | Category |
|---|---|
| Aadhaar Card or another valid identity proof, where required | Personal document |
| Recent passport-size photograph | Personal document |
| Valid email address | Personal document |
| Active mobile number | Personal document |
| Address proof, if required | Personal document |
| Class 10th Marksheet and Certificate | Academic document |
| Class 12th Marksheet and Certificate | Academic document |
| Graduation Marksheet and Degree Certificate, if applicable | Academic document |
| Postgraduate Marksheet and Degree Certificate, if applicable | Academic document |
| Transfer Certificate, Migration Certificate or other documents, if required | Academic document |
| Category Certificate, if applicable | Additional document |
| Work Experience Certificate, if required | Additional document |
| Entrance Examination Scorecard, if applicable | Additional document |
| Other documents specified by the institution | Additional document |
UGC Refund Policy
We believe students should have access to transparent admission and fee-related information before making an educational decision. For eligible higher education institutions, fee refunds are governed by the applicable University Grants Commission (UGC) notifications and the institution's officially published refund policy.
1. Important Refund Guidelines
| Guideline 1 | Students should submit a formal admission withdrawal or cancellation request through the prescribed institutional process. |
|---|---|
| Guideline 2 | Refund eligibility depends on the applicable UGC policy, admission schedule and date of withdrawal. |
| Guideline 3 | The institution must follow the applicable refund percentages and permitted deductions. |
| Guideline 4 | Refund processing timelines are subject to the applicable UGC rules and institutional procedures. |
| Guideline 5 | Original academic documents must not be retained by the institution in violation of applicable UGC regulations. |
2. General Refund Schedule (UGC October 2018 Framework)
| Withdrawal timeline | Refund |
|---|---|
| Withdrawal 15 days or more before the formally notified last date of admission, subject to the permitted processing deduction | 100% |
| Withdrawal less than 15 days before the last date of admission | 90% |
| Withdrawal within 15 days after the last date of admission | 80% |
| Withdrawal more than 15 days but within 30 days after the last date of admission | 50% |
| Withdrawal more than 30 days after the last date of admission | 0% |
Official document: View UGC Refund Policy (PDF)
GenAI & Agentic AIReviews & Ratings
No reviews have been published for this program yet.
Let’s clear up some doubts
Can I fit GenAI & Agentic AI around my work or other commitments?
It may be possible, but the timetable matters. Before enrolling in GenAI & Agentic AI, check live-session timings, attendance rules, assignment deadlines and exam dates against your work and personal commitments.
What should I look for in the syllabus for GenAI & Agentic AI?
Ask for an itemised cost breakdown for GenAI & Agentic AI, including tuition, registration, examination, software, materials and any other compulsory charges. Confirm instalment dates, cancellation rules and refund terms before paying.
How can I make a shortlist for GenAI & Agentic AI?
Recognition depends on the provider, qualification and intended use. Check relevant official regulator or accreditor records and, if needed, confirm acceptance with the employer, licensing body or institution where you plan to use the qualification.
What practical outcomes could GenAI & Agentic AI help me work towards?
Some courses welcome beginners, while others assume prior study or professional experience. Check the prerequisite list for GenAI & Agentic AI and ask the provider whether preparatory learning is recommended if you are new to the subject.
Are there exams or assessments in GenAI & Agentic AI?
Ask how to contact faculty or trainers, access learning materials, get technical help and resolve academic questions. For online learning, also confirm whether sessions are recorded and how long you can access course resources.
What should I know before choosing GenAI & Agentic AI?
Start with the exact outcome you want from GenAI & Agentic AI, then check the current course outline, entry rules, schedule and total cost. The title alone may not tell you what is included, so ask the provider to clarify anything important before you commit.
How can I compare providers offering GenAI & Agentic AI?
There is no single duration that applies to every provider. Check the published study period, term or semester structure, pace options and any maximum completion window for GenAI & Agentic AI before planning your schedule.
Can beginners apply for GenAI & Agentic AI?
The course may help you build knowledge relevant to GenAI & Agentic AI, but career results depend on your existing skills, experience, portfolio and employer expectations. Check the actual modules and practical components rather than assuming a particular job outcome.
Who is GenAI & Agentic AI most suitable for?
Ask for the latest syllabus, entry criteria, total fee, delivery schedule, assessment method and completion requirements. For professional training, also confirm whether an exam voucher or certificate fee is included in the advertised price.
What costs should I check before enrolling in GenAI & Agentic AI?
Look for a clear module list, learning outcomes, hands-on assignments and a sensible progression from foundational to advanced topics. For GenAI & Agentic AI, confirm that the syllabus is current and covers the areas you specifically want to learn.