Professional Certificate Programme in Agentic AI and Applications

• Organising code with modules and packages

• How apps talk to each other

• Reading and writing data

• Analyzing data with Pandas

• Working with CSV and Excel files, AI-assisted coding and debugging

• Skills:

  • Python modules

  • Pandas

  • AI-assisted coding

  • APIs

  • CSV/Excel

• Learning Outcome:

  • Use Python, APIs, and data tools to work with data and build simple application components.

• How AI has evolved over time

• What machine learning is

• What deep learning is

• What foundation models are

• What generative AI is

• Where AI is used across industries

• Using AI responsibly at work

• Skills:

  • AI

  • Deep learning

  • Generative AI

  • ML

  • Foundation models

• Learning Outcome:

  • Understand the fundamentals of AI, ML, deep learning, foundation models, and Generative AI and where they are applied.

• Evolution of neural networks and computational models

• Activation functions and learning mechanisms

• RNNs, LSTMs and GRUs for sequence modeling

• GANs for generative AI

• Autoencoders for representation learning

• Transformers, foundation models and LLM architecture

• Model Context Protocol (MCP) for tool integration and agentic AI systems

• Skills:

  • Neural networks

  • Transformers

  • MCP

  • Deep learning architectures

  • Foundation models

• Learning Outcome:

  • Understand key architectures behind modern AI and Generative AI models and how they support AI applications.

• How LLMs evolved

• A plain-language look at transformer architecture

• What tokens and tokenization are

• What embeddings are

• What a context window is

• How models are trained, fine-tuned and used

• How models call outside tools

• Choosing the right model

• Balancing cost and performance

• Skills:

  • LLMs

  • Tokens

  • Context windows

  • Transformers

  • Embeddings

  • Model selection

• Learning Outcome:

  • Understand how LLMs process information, generate responses, use context, and interact with external tools.

• Core principles of good prompting

• Zero-shot, one-shot and few-shot prompting explained

• Writing effective system prompts

• Chain-of-thought prompting

• Building reusable prompt templates

• Structuring prompts for consistency

• Testing and improving your prompts

• Skills:

  • Prompt design

  • Zero-shot

  • Few-shot prompting

  • System prompts

  • One-shot

  • Prompt testing

• Learning Outcome:

  • Create and refine prompts to produce more useful and consistent AI outputs.

• Generating text

• Generating code

• Generating images

• Generating audio

• Generating video

• AI copilots

• How enterprises use generative AI

• Understanding limitations and hallucinations

• Prompt templates introduction

• Skills:

  • Text

  • Image

  • AI copilots

  • Code

  • Audio and video generation

• Learning Outcome:

  • Explore common Generative AI applications and understand their capabilities and limitations.

• What embeddings are

• How AI measures similarity between pieces of text

• Ways to measure “distance” between ideas

• What vector databases are

• A look at FAISS

• Combining search methods (hybrid search)

• Improving results (re-ranking)

• Filtering by metadata

• How enterprises architect search

• Skills:

  • Embeddings

  • Vector databases

  • Hybrid search

  • Semantic similarity

  • FAISS

  • Reranking

• Learning Outcome:

  • Understand how embeddings and vector search help AI applications find relevant information.

• Why AI needs retrieval-augmented generation (RAG)

• How a RAG system is put together

• Loading documents

• Breaking documents into chunks

• Building an embeddings pipeline

• Retrieving relevant content

• Generating grounded answers

• The full RAG lifecycle

• Skills:

  • RAG

  • Chunking

  • Retrieval

  • Document processing

  • Embeddings

• Learning Outcome:

  • Understand and build the basic components of a RAG workflow to generate responses grounded in relevant data

• What an AI agent is

• How an agent is structured

• The agent lifecycle

• How agents plan, remember, use tools and reason

• Different types of AI agents

• How enterprises use agents

• Skills:

  • Agent concepts

  • Planning

  • Memory

  • Architectures

  • Reasoning

  • Tools

• Learning Outcome:

  • Understand how AI agents are structured and how they use planning, reasoning, memory, and tools to perform tasks.

• LangGraph basics

• The building blocks – nodes and edges

• Managing what an agent “remembers” mid-task (state)

• Designing agent workflows

• Keeping a human in the loop

• Connecting tools to your agent

• Tools:

  • LangGraph

  • Edges

  • Workflows

  • Human-in-the-loop

  • Nodes

  • State

  • Tools

• Learning Outcome:

  • Build a simple AI agent workflow using an agent framework, tools, state, and human oversight.

• Why use multiple agents together

• Common collaboration patterns

• Supervisor agents

• Planner-worker setups

• How agents communicate

• Delegating tasks between agents

• Resolving conflicts

• Orchestrating agents at enterprise scale

• Skills:

  • Multi-agent architectures

  • Delegation

  • Collaboration

  • Orchestration

• Learning Outcome:

  • Understand common multi-agent patterns and design workflows in which agents collaborate and delegate tasks.

• The ReAct framework (reason + act)

• Planning strategies for agents

• Reflection – agents reviewing their own work

• Tree of Thoughts and Graph of Thoughts approaches

• How agents self-correct

• A look at reasoning models

• How enterprises apply these patterns

• Tools:

  • ReAct

  • Reflection

  • Self-correction

  • Planning

  • Reasoning patterns

• Learning Outcome:

  • Understand and apply common reasoning and planning approaches used in AI agent workflows.

• Short-term vs. long-term memory in agents

• How agents remember a conversation

• What MCP is and why it matters

• A look at MCP architecture

• Context engineering explained

• Common memory design patterns

• How enterprises manage agent memory

• Tools:

  • Agent memory

  • MCP

  • Context engineering

  • Tool integration

• Learning Outcome:

  • Understand how agents manage memory and context and how MCP enables interaction with external tools and systems.

• How to evaluate an agent's performance

• Detecting hallucinations

• Engineering for reliability

• Setting up guardrails

• Safety controls

• Incorporating human feedback

• Evaluating agents with LangSmith

• Key evaluation metrics

• Quality assurance at enterprise scale

• Skills:

  • Agent evaluation

  • Tracing

  • Guardrails

  • Metrics

  • Hallucination detection

• Learning Outcome:

  • Evaluate agent performance and apply basic guardrails and feedback approaches to improve reliability.

• What AI governance means

• Principles of responsible AI

• Understanding prompt injection attacks

• Preventing data leakage

• Privacy considerations

• Compliance basics

• Enterprise AI policies

• Managing AI risk

• Skills:

  • AI governance

  • Data leakage

  • Compliance

  • Prompt injection

  • Privacy

  • Risk

• Learning Outcome:

  • Understand key risks associated with AI agents and the principles used to support secure and responsible AI use.

• Deploying agents with FastAPI

• Publishing an API

• Monitoring agent performance

• Logging

• Tracing with LangSmith

• Optimising performance

• Managing costs

• Skills:

  • FastAPI

  • Logging

  • Monitoring

  • Cost

  • APIs

  • Tracing

  • Performance

• Learning Outcome:

  • Understand common approaches for making AI agents accessible through APIs and monitoring their performance.

• Options for hosting agents (cloud, serverless, embedded)

• A guided walkthrough of API deployment

• Latency optimisation basics

• Monitoring principles explained

• A live demo publishing an endpoint with FastAPI/Swagger

• Setting up Docker Desktop

• Monitoring with tracing tools (OpenTelemetry, LangSmith)

• An instructor-led deployment demo

• Real-world case studies across industries

• ROI of adopting agents

• Success stories and lessons learned

• Skills:

  • Deployment approaches

  • APIs

  • Tracing

  • ROI

  • Docker

  • Monitoring

  • Case studies

• Learning Outcome:

  • Understand key considerations involved in taking an AI agent from a working application toward practical use and review real-world examples.

• Project guidelines and a success checklist

• Ready-made project templates (research assistant, Q&A bot, planning assistant, tutor, etc.)

• A guided capstone build combining prompting, basic agent frameworks, RAG-based document Q&A, and at least one tool into one working assistant

• Tips on deployment and UI wrapping

• Project planning and peer idea reviews

• Mentorship breakout rooms

• Final presentations and feedback

• Group reflection

• Guidance choosing Track 1 or Track 2

• Skills:

  • Problem definition

  • Prompting

  • Tools

  • Agent design

• Learning Outcome:

  • Design and build a functional AI agent that brings together relevant techniques covered in the program to address a defined problem.

Module S2.1: Agentic Thinking: Designing Smarter Workflows

• How LLMs differ from traditional rule-based systems

• How AI agents make contextual decisions

• Systems thinking for workflow design

• Human-centered process design

• Mapping the customer journey

• Spotting where an agent could help (opportunity identification)

• Breaking a workflow into steps

• Common agent design patterns (sequential, planner-executor, supervisor)

• Keeping a human in the loop

• Service blueprinting

• Defining KPIs

• Guardrails and risk controls

• Process mapping and business workflow transformation

Module S2.2: The Enterprise AI Agent Toolkit and Landscape

• Automating workflows with no-code tools (n8n, Zapier, Make, Flowise)

• Trigger-based automation

• Connecting tools and APIs

• Common business process automation patterns

• How agent teams collaborate (hierarchical vs. peer-to-peer, manager-worker)

• How humans and agents work together

• Keeping context flowing across a workflow (shared memory and handoffs)

• Designing workflows that scale

• Hands-on builds: a lead-qualification workflow, a support-ticket triage workflow, and an approval workflow

Module S2.3: No-Code Agent Platforms and Enterprise Tools

• Common categories of enterprise agents (research, meeting, document-intelligence, knowledge-assistant)

• Approval and

  escalation workflows

• How humans and agents collaborate

• Copilots for sales, marketing and

  customer support

• Connecting to ERP and CRM systems

• Governance, access control, auditability and

  compliance

• Feeding an agent the right context at the right time

• A look at enterprise platforms – Salesforce Agentforce, Microsoft Copilot Studio, AWS Bedrock Agents, Google Vertex AI Agents

• Assessing automation readiness

• Evaluating vendors

• Build-vs-buy decisions

• Choosing your enterprise AI stack

Module S2.4: Governance, Risk and Operating Models for AI Agents

• Agent use cases across industries (business, education, healthcare, customer support, research) with a workflow-automation focus

• ROI of adopting agents

• Success stories and lessons from failures

• The basics of calculating ROI

• Case-study analysis and group discussion

Module S2.5: Case Studies, ROI and Vendor Strategy

• AI guardrails and

  risk controls, explained in business terms

• Calculating ROI and building a business case

• Designing an operating model

• Setting up a governance framework

• Assessing risk

• Building an adoption roadmap

• Human override mechanisms

• Designing for human-in-the-loop

Module S2.6: Capstone: End-to-End Enterprise Agentic Workflow

• Identifying a real business problem

• Mapping the as-is and to-be journey

• Analyzing where an agent could help

• Designing a multi-agent workflow

• Passing context and coordinating between agents

• Building the workflow automation

• Integrating with enterprise systems

• Defining KPIs and success measures

• Guardrails and

  failure management

• Human override mechanisms

• Assessing business impact

• Optimising for productivity and cost

• Deploying the workflow

• A full end-to-end demonstration

• Limitations and Ethical Issues of Generative AI

• Social and Economic Impact and Responsible Generative AI

• Generative AI in Business: Trends, Ideas, and Implementation

• Generative AI: Impact and Opportunities for Career Growth

You will work hands-on with 30+ cutting-edge tools and frameworks in Agentic AI, including:

• LangChain

• CrewAI

• AutoGen

• Flowise

• OpenAI APIs

• ChromaDB

• Hugging Face

• Pinecone

• Streamlit

These tools support the design and deployment of autonomous agents, vector-based search systems, and Retrieval-Augmented Generation (RAG) pipelines.

Absolutely. This programme is designed for beginners, including non-coders and professionals from domains like finance, HR, marketing, and education. The curriculum begins with Python fundamentals and gradually builds toward advanced Agentic AI concepts, making it ideal for anyone looking to upskill in the most in-demand artificial intelligence and machine learning capabilities.

Graduates will be equipped to take on roles such as:

• AI Agent Developer

• Prompt Engineer

• Automation Strategist

• AI Product Owner

• LLM Application Specialist

With growing demand in domains like finance, customer support, legal tech, and education, Agentic AI opens doors to future-proof, high-growth careers.

Unlike generic LLM or GenAI courses, this programme focuses on building intelligent agents that can autonomously use tools, store memory, retrieve documents, and interact through reasoning. You’ll learn how to implement agentic workflows using modular prompts, memory chains, and tool orchestration, which are not commonly covered in other short-term courses.

You’ll complete 20+ hands-on assignments and a final capstone project, where you’ll build your own deployable AI agent. Projects include creating document Q&A bots, itinerary planners, memory-enhanced assistants, and goal-based automation agents using LangChain, AutoGen, and Flowise.

Upon successful completion, you will receive a Certificate of Completion from IITM Pravartak (the tech innovation hub of IIT Madras). Additionally, you will earn 3 IBM digital certifications, adding strong global credibility to your AI portfolio.

Choosing the best Agentic AI course for working professionals depends on your career goals, technical background, and how you plan to apply AI in real-world scenarios. Key factors to consider include the institution’s credibility, curriculum depth, hands-on learning with industry tools, and the ability to build and deploy real AI agents.

The Professional Certificate Programme in Agentic AI and Applications by IITM Pravartak is designed specifically for working professionals looking to move beyond basic AI usage and build autonomous AI systems. Delivered in a flexible 5-month online format, the programme focuses on practical implementation using tools such as LangChain, CrewAI, AutoGen, and Flowise.

Participants gain hands-on experience through 20+ assignments and a capstone project where they build a fully functional AI agent. The programme also includes domain expert-led sessions, select masterclasses, and three IBM certifications in AI Agent Building, Generative AI for Business, and Responsible AI.

This combination of structured learning, real-world application, and flexible delivery makes it a strong choice for professionals aiming to build practical Agentic AI skills while continuing to work. The programme also has a successful legacy of 1500+ learners with a 4.4/5 rating.

Salaries for Agentic AI professionals in India are seeing a significant rise as demand for AI agents, automation systems, and GenAI applications grows across industries.

Based on recent industry trends:

• Prompt Engineers / LLM Specialists: ~35% to 45% salary increase

• RAG Workflow Engineers: ~50% higher compensation

• Agentic AI Developers (LangChain, AutoGen, etc.): ~60% salary growth

• GenAI Project Contributors: Up to 2x faster promotion rates

• AI-skilled professionals vs standard developers: ~30% higher pay

These roles are in high demand because organisations are moving from simple AI usage to building autonomous systems that can plan, reason, and execute tasks.

As more companies adopt Agentic AI for automation, decision-making, and workflow optimisation, professionals with hands-on experience in tools like LangChain, CrewAI, AutoGen, and RAG systems are commanding premium salaries.

This makes Agentic AI one of the fastest-growing and highest-paying skill areas in today’s AI landscape.

An Agentic AI course can be a worthwhile investment for professionals who want to move beyond basic AI usage and build systems that can automate tasks, make decisions, and execute workflows. As organisations shift toward AI-driven operations, the ability to design and deploy AI agents is becoming a highly valuable skill.

A well-designed Agentic AI programme provides a strong foundation in areas such as AI agents, Retrieval-Augmented Generation (RAG), memory systems, and multi-agent workflows, along with the flexibility needed by working professionals.

The Professional Certificate Programme in Agentic AI and Applications by IITM Pravartak combines structured learning with practical implementation, making it relevant for professionals across technical, product, and business roles. With hands-on assignments, real-world projects, and a capstone where participants build a working AI agent, the programme focuses on application rather than just theory.

This balance of practical skills, industry-relevant tools, and flexible learning makes an Agentic AI course a valuable step for professionals looking to stay relevant and grow in an AI-driven workplace.

The Agentic AI course by IITM Pravartak offers strong value for professionals looking to move beyond basic AI usage and build real-world automation and decision-making systems. It is particularly relevant for those who want to work with AI agents, design intelligent workflows, or apply AI more effectively in their roles.

The Professional Certificate Programme in Agentic AI and Applications by IITM Pravartak is designed with a strong focus on practical implementation. It is delivered by experienced domain experts, with weekly live masterclasses from IITM Pravartak faculty (over and above the core curriculum), and covers key areas such as AI agents, multi-agent systems, Retrieval-Augmented Generation (RAG), memory, and agent deployment.

The programme has already seen 1500+ learners successfully enrol, reflecting strong demand for Agentic AI skills across industries. Its 5-month online format provides flexibility for working professionals, while hands-on assignments, real-world projects, and a capstone agent build ensure that learning is directly applicable to real scenarios.

Participants also earn a certificate from IITM Pravartak along with three IBM certifications, strengthening their industry credibility.

This combination of applied learning, proven learner trust, and industry-relevant tools makes it a valuable option for professionals aiming to build practical Agentic AI capabilities and advance their careers.

An Agentic AI and Applications course helps professionals move beyond basic AI usage and learn how to build systems that can plan, reason, and execute tasks autonomously. As organisations increasingly adopt AI for automation and decision-making, these skills are becoming highly valuable across roles.

The Professional Certificate Programme in Agentic AI and Applications by IITM Pravartak is designed to deliver these benefits through hands-on, application-focused learning.

Key benefits include:

• Ability to build AI agents that can automate multi-step workflows

• Hands-on experience with tools like LangChain, CrewAI, AutoGen, and Flowise

• Practical understanding of concepts like RAG, memory systems, and agent orchestration

• Opportunity to develop real-world projects and a capstone AI agent

• Industry-recognised credentials from IITM Pravartak and 3 IBM certifications

• Flexibility to learn while continuing to work

With 1500+ learners already enrolled, the programme reflects strong demand for Agentic AI skills across industries.

Overall, an Agentic AI course equips you with the ability to design intelligent systems that improve efficiency, reduce manual effort, and deliver real business impact.

Choosing the right Agentic AI course in 2026 depends on how well it prepares you to build and deploy real AI agents-not just understand concepts. As demand grows, it’s important to focus on programmes that offer practical, hands-on learning aligned with industry needs.

Here are key factors to consider:

• Hands-on learning: Look for courses that include assignments, projects, and a capstone where you build real AI agents

• Relevant tools: Ensure exposure to tools like LangChain, CrewAI, AutoGen, Flowise, and RAG frameworks

• Curriculum depth: The course should cover agent design, memory, planning, and multi-agent systems

• Faculty & delivery: Programmes with domain expert-led sessions and structured guidance are more effective

• Flexibility: A format that allows you to learn alongside work is important for consistency

• Industry recognition: Certifications from reputed institutions and organisations add credibility

The Professional Certificate Programme in Agentic AI and Applications by IITM Pravartak aligns well with these criteria. It offers a 5-month structured learning journey, hands-on assignments, a capstone agent build, and certifications from IITM Pravartak along with three IBM credentials.

With 1500+ learners already enrolled, it reflects strong trust and relevance in the evolving Agentic AI space.

Choosing the right course ultimately comes down to one thing-how well it helps you apply AI in real-world scenarios, not just understand it.