Programme Content
Begin with an overview of the program structure, curriculum roadmap, and expected learning outcomes. The session introduces the key concepts covered throughout the program and examines the shift from traditional software systems to AI-native applications, intelligent agents, and autonomous workflows. You will also explore how enterprises are leveraging AI to transform products, operations, and decision-making at scale.
Build a strong foundation in Python for AI application development. This module covers core programming concepts, data structures, functions, OOP, file handling, and error management, while introducing AI-assisted development practices. Learn how to leverage modern coding assistants for code generation, debugging, optimization, and rapid prototyping to accelerate the development of AI-powered solutions.
Explore the technological foundations driving modern AI systems. This module covers the evolution of AI, the architecture behind transformers and large language models, and the emergence of agentic systems capable of reasoning, planning, and action. You will also examine how AI agents fit within the broader GenAI stack and enable the development of intelligent, autonomous applications.
Go beyond basic prompting to build intelligent, context-aware AI applications. This module covers advanced prompt engineering, LLM optimization and evaluation, fine-tuning techniques, and Retrieval-Augmented Generation (RAG). Using LangChain, you will learn to develop sophisticated AI applications that combine reasoning, retrieval, and customization to deliver reliable, domain-specific outcomes.
Learn how AI agents interact with tools, manage context, and execute complex tasks through the Model Context Protocol (MCP). This module covers MCP architecture, tool integration, agent communication, and context management, while addressing key considerations around authentication, security, and reliability. You will build and test context-aware agents capable of operating across diverse tools and environments.
Practice building Generative AI solutions on Azure from setup to evaluation. Cover AI environments, model deployment, Foundry SDK, prompt flow, RAG workflows, fine-tuning, responsible AI, and Azure AI Studio tools used to test and improve application performance.
Discover how collaborative AI systems solve complex tasks through coordination, memory, and orchestration. This module covers multi-agent architectures, Graph RAG, and agent workflow design using CrewAI and LangGraph. Learn how to define agent roles, manage state and memory, and orchestrate intelligent workflows that enable multiple agents to work together effectively in enterprise environments.
Learn how to take AI applications from prototype to production with robust deployment and operational practices. This module covers LLMOps, cloud infrastructure, deployment strategies, monitoring, optimization, and governance. Using AWS-based workflows, you will explore how to build, deploy, scale, and maintain enterprise-grade AI systems while ensuring security, reliability, and operational efficiency.
Deploy and orchestrate AI agents using modern hosting, n8n workflows, and production-ready operational patterns. To monitor, improve, and showcase products, implement event logging, analytics, feedback loops, and end-to-end integrations. Anchor this execution in the foundations of AI governance and responsible AI. By ensuring fairness, bias mitigation, privacy, transparency, and human oversight, you can manage AI risk, meet regulatory compliance, and build robust enterprise frameworks.
Learn how to connect AI agents with enterprise knowledge using Foundry IQ. You'll explore how Retrieval Augmented Generation (RAG) solves the knowledge problem for AI agents, discover how Foundry IQ provides a shared knowledge platform that multiple agents can access, improve retrieval quality through data optimization, and configure agent instructions for consistent, cited responses.
Put your learning into a full-scale AI product project. Design a 4-agent market research and GTM framework using n8n, CrewAI, and MCP integration, while connecting agent performance data with Lean Canvas planning, pricing models, acquisition strategy, and real-world chatbot deployment.
Join an interactive online Masterclass delivered by eminent IIT Kanpur faculty. Explore recent developments, emerging technologies, and academic perspectives through expert-led discussions that connect advanced concepts with real-world relevance.
AI Impact Studio is an exclusive workshop series keeping learners ahead of the AI curve — live, expert-led sessions on Generative AI, Agentic AI, and automation. Get hands-on with tools like Claude, n8n, OpenClaw, LangChain in the Builder track, or apply AI to decisions, productivity, and growth in the Business track. Real labs, real use cases, and career-ready insights — value that goes far beyond the core curriculum.
Learn to architect and govern AI agent systems by mapping agent responsibilities across the SDLC, defining structured tasks with clear inputs, outputs, and success criteria, and separating planning, reasoning, and execution for reliable, inspectable workflows. Master production-ready practices including PR-based governance, workflow orchestration, cross-job coordination, observability, tool governance, secrets management, and reliability patterns for safe agent operations.
