Applied AI & Agentic AI Course by IIIT Bangalore and upGrad: Why This Programme Is Worth Considering
Artificial Intelligence is no longer limited to research labs or large technology companies. AI tools are now becoming part of everyday business—from customer support and marketing to software development, finance, healthcare and operations.
But there is a new shift happening.
AI is moving from systems that simply answer questions or make predictions towards systems that can plan tasks, use tools, retrieve information and complete a series of actions. This is where Agentic AI comes into the picture.
For people who want to build a career around this changing technology, learning only prompt writing or basic Generative AI may not be enough. A stronger understanding of Machine Learning, AI engineering, deployment, LLMs and AI agents can provide a much broader foundation.
This is the idea behind the Executive Post Graduate Programme in Applied AI & Agentic AI by IIIT Bangalore, powered by upGrad.
The programme runs for 30 weeks and combines live and recorded learning, practical projects and two major capstone projects.
Executive Post Graduate Programme in Applied AI & Agentic AI – IIIT Bangalore
Build the future of AI with IIIT Bangalore’s Executive Post Graduate Programme in Applied AI & Agentic AI. A 30-week live + recorded programme covering Machine Learning, Deep Learning, MLOps, LLMs, RAG, AI system design and autonomous multi-agent systems. Includes 30+ projects, 2 capstones, 150+ live hours, industry-focused learning, certification and career support.
What Is the Applied AI & Agentic AI Programme?
In simple words, this is a programme designed to teach learners how AI systems are built, deployed and developed into more autonomous applications.
Instead of jumping directly into AI agents, the curriculum starts with the basics.
Learners first work with:
Python → Data → Machine Learning → Deep Learning → MLOps
After that, the programme moves towards:
LLMs → RAG → AI System Design → AI Agents → Multi-Agent Systems
This is important because an AI agent is not just a chatbot with a different name. Building useful agentic systems requires an understanding of models, data, APIs, tools, system architecture, security and deployment.
The programme is therefore divided into two broad areas: Applied AI and Agentic AI.
Why Is Agentic AI Becoming Important?
Generative AI has changed the way people interact with technology.
A user can ask an AI model to write an email, explain a document, generate code or summarise information. But many real-world business problems involve more than producing a single response.
Imagine an AI system that has to:
- Understand a request
- Search relevant information
- Decide what needs to be done
- Use different tools
- Complete several steps
- Check the result
- Ask for human approval when required
That is closer to the idea of an AI agent.
The programme introduces concepts such as planning, memory, tool use and safety, followed by practical exposure to frameworks including LangChain, LlamaIndex and LangGraph.
For someone who wants to understand where AI development is heading, this is one of the most interesting parts of the course.
What Will You Actually Learn?
One of the reasons this programme stands out is its progression.
Rather than treating AI as a single subject, it breaks the learning journey into different stages.
1. Start With Python and Data
Before building advanced AI applications, learners need to understand how data is handled.
The first course covers Python OOP, NumPy, Pandas, SQL, feature engineering, data preprocessing, EDA, visualisation and Git/GitHub.
Why does this matter?
AI systems depend heavily on data.
If the data is poorly prepared, even a sophisticated Machine Learning model can produce poor results. Understanding data processing therefore gives learners a foundation that remains useful across different AI technologies.
2. Learn Machine Learning and Deep Learning
The next stage moves into traditional Machine Learning and Deep Learning.
The curriculum includes regression, classification, decision trees, boosting, XGBoost, LightGBM, clustering, PCA, model evaluation and explainability.
The Deep Learning section introduces neural networks, CNNs, transfer learning, RNNs and LSTMs.
Why learn this when Generative AI is popular?
Because not every business problem requires an LLM.
Some problems are better solved with traditional Machine Learning models. Understanding both approaches helps professionals decide which technology should be used for which problem.
That is an important skill in practical AI development.
3. Move From Models to Production With MLOps
Creating a model inside a notebook is one thing.
Running that model reliably for real users is something else.
The programme addresses this through its MLOps and Production Systems module.
Learners explore MLflow, model training, hyperparameter optimisation, containerisation, FastAPI serving, A/B testing, model drift detection, monitoring, governance and ROI calculations.
Why choose a course that teaches MLOps?
Because companies don’t only need people who can build models. They also need people who understand what happens after the model is built.
Deployment, monitoring, cost and reliability become important when AI moves from an experiment into a production environment.
This production-focused approach is one of the stronger reasons to consider the programme.
4. Understand LLMs and RAG
The programme then moves into the world of Large Language Models.
Learners explore NLP, Transformers, LLM APIs, prompt engineering, embeddings, vector databases, hybrid search and RAG.
It also covers more advanced areas such as query rewriting, multi-hop reasoning, citation, RAG evaluation and fine-tuning methods including LoRA, QLoRA and PEFT.
What is RAG in simple language?
RAG, or Retrieval-Augmented Generation, allows an AI application to retrieve relevant information and use that information while generating an answer.
For example, instead of asking an AI model to answer a question only from its existing knowledge, an organisation could build a system that retrieves information from its own documents and then uses that information to produce a response.
That makes RAG particularly relevant when building AI applications around business or organisational information.
5. Learn How to Design AI Systems
Building an AI application is not only about getting the model to work.
There are other questions:
How much will it cost?
Can it handle more users?
How should it be evaluated?
How do you protect it from attacks?
How should sensitive information be handled?
The programme’s AI System Design module addresses these areas through topics including token economics, cost optimisation, LLM architecture, evaluation, load balancing, caching, prompt-injection defence, privacy, audit logging, IAM and human-in-the-loop systems.
This is another reason the course can appeal to professionals who want to move beyond experimentation and understand the wider AI engineering process.
6. Finally, Build Agentic AI Systems
The final part of the curriculum focuses directly on AI agents and orchestration.
Learners explore:
- ReAct
- Tool use
- Function calling
- Memory
- Planning
- Goal decomposition
- LangChain
- LangGraph
- LlamaIndex
- AutoGen
- CrewAI
- Telemetry
- Observability
- AI compliance
The objective is not simply to understand what an AI agent is, but to learn how different components can be organised into an autonomous or multi-agent system.
The Biggest Advantage: Two Capstone Projects
One of the most practical elements of the programme is that learners get two major capstone projects instead of finishing with only theoretical knowledge.
Capstone 1: Applied AI
The first capstone focuses on an end-to-end Machine Learning system.
Learners work around areas such as:
- Data pipelines
- MLOps
- Model workflows
- Monitoring
- Performance
The expected output is a fully functional ML pipeline.
Capstone 2: Agentic AI
The second capstone takes the learning into autonomous systems.
Learners design and develop an Autonomous Multi-Agent System, with technologies such as LangChain and LlamaIndex and a focus on agent orchestration.
The intended outcome is a fully functional multi-agent system.
Why are two capstones useful?
Because they show two different sides of AI.
The first demonstrates that you can work with Machine Learning and production systems.
The second demonstrates exposure to LLMs, orchestration and Agentic AI.
That combination can make a portfolio more interesting than a collection of basic AI tutorials.
More Than 30 Projects
The programme includes 30+ projects, assignments and hands-on activities.
The brochure presents examples from several industries.
These include:
- Healthcare
- Banking and financial services
- Retail
- Real estate
- Energy
- Automotive
- Legal technology
- Agriculture
- Travel
- Manufacturing
- Media
- Education
For example, project ideas include an AI compliance officer, conversational shopping agent, AI paralegal, autonomous travel concierge, supply-chain response system and adaptive AI tutor.
The brochure also makes clear that these are indicative projects and that specific project details are provided during the programme.
Which Tools Will You Work With?
The programme covers a wide range of technologies.
Programming & Data
Python, NumPy, Pandas, Matplotlib, Seaborn and Git.
Machine Learning
ML and Deep Learning frameworks and interpretability tools.
MLOps & Deployment
MLflow, FastAPI and cloud/deployment technologies.
Generative AI
LLMs, APIs, embeddings, vector databases and RAG.
Agentic AI
LangChain, LangGraph, LlamaIndex, AutoGen and CrewAI.
AI Security
Privacy, audit logging, IAM, AI compliance and cost optimisation.
The benefit of this broad stack is that learners can see how different technologies fit together rather than learning each tool in isolation.
What About Certification?
The programme offers a Programme Completion Certificate from IIIT Bangalore, according to the brochure. It also mentions an IIITB ML & AI alumni network of more than 10,000 professionals.
There are also Microsoft-associated certification modules covering:
- Generative AI concepts
- GitHub Copilot
- Power BI analytics solutions
- Data Science solutions on Azure
For learners, the certificate can add an additional credential to their professional profile, although the real value of the programme will ultimately depend on the skills and projects they build during the course.
Learning Isn’t Only About Recorded Videos
The programme includes 150+ live hours across 30 weeks, with live classes forming a significant part of the learning structure.
The brochure also lists:
- Industry expert sessions
- IIITB faculty sessions
- Daily doubt-resolution sessions
- Career coaching
- Technical mock interviews
- HR mock interviews
- Resume support
- LinkedIn optimisation
- Curated job opportunities
- Networking opportunities
This can be useful for learners who prefer a structured programme with support rather than studying entirely on their own.
Who Should Consider This AI Programme?
This course may be worth considering if you are:
A software professional
You want to add AI and Machine Learning capabilities to your existing technical background.
A data professional
You want to move from analytics or data science towards Generative AI and Agentic AI.
An aspiring AI/ML professional
You want a structured route covering fundamentals through advanced AI applications.
A professional exploring Generative AI
You want to go beyond basic prompting and understand LLMs, RAG and AI application design.
Someone interested in AI agents
You want to learn about agent frameworks, orchestration and multi-agent systems.
The brochure lists graduates and undergraduates, preferably in their final year, among the intended learner groups.
Why Choose This Course?
There are many AI courses available today, so choosing one should not be based only on the words “Generative AI” or “Agentic AI” in the title.
Here are some practical reasons this programme may be worth considering.
1. It Covers More Than Generative AI
The programme does not start and end with ChatGPT-style applications.
It covers the journey from Python and Machine Learning through MLOps, LLMs, RAG and Agentic AI.
2. It Combines Applied AI and Agentic AI
Instead of choosing between traditional AI and newer agent technologies, the programme brings both together.
3. It Focuses on Practical Work
With 30+ projects and two capstones, there is a strong emphasis on applying the concepts rather than only studying them.
4. It Includes Production Concepts
MLOps, monitoring, governance, security and cost optimisation are part of the curriculum.
That matters because real-world AI is about more than getting a model to work once.
5. It Goes Deep Into Agentic AI
The programme dedicates a substantial section to agents, orchestration, memory, planning, tools and multi-agent systems.
6. You Build Two Different Types of Projects
The two capstones allow learners to demonstrate both Applied AI and Agentic AI capabilities.
7. There Is Live Learning and Career Support
For learners who need structure and interaction, live sessions, doubt resolution and career support can be useful additions.
Is This Course Right for Everyone?
Probably not.
If you only want to learn how to use AI tools for everyday tasks, a 30-week programme may be more than you need.
Likewise, someone who already has extensive experience building and deploying AI systems may find parts of the foundational curriculum familiar.
This programme makes more sense for people who want a broader and structured AI learning journey, particularly those interested in moving towards AI engineering, Machine Learning, Generative AI or Agentic AI.
Before enrolling, prospective learners should also verify the latest programme fees, schedule, eligibility requirements, admission conditions and other terms directly with the provider.
In Final
The AI industry is changing quickly. Learning one AI tool today may not be enough to stay relevant tomorrow.
The more useful approach can be to understand the fundamentals behind the technology—how data is prepared, how models work, how systems are deployed, how LLM applications retrieve information and how AI agents can be designed to perform tasks.
That is where the Applied AI & Agentic AI Programme by IIIT Bangalore and upGrad tries to position itself.
From Python and Machine Learning to MLOps, LLMs, RAG and multi-agent systems, the programme brings several layers of modern AI into one structured learning path.
Its 30-week format, 150+ live hours, 30+ projects and two capstone projects make it particularly relevant for learners looking for a practical and structured way to explore the next generation of AI.
The most important question, however, is not simply whether Agentic AI is the next big thing.
It is whether you want to understand how these systems actually work—and how to build them.
