Numbers, Strings, Lists, Dictionaries, Loops and conditionals in Python;  Live coding: Build a decisionmaking chatbot; Loop exercises and variable tracing

Defining functions, Arguments and returns, Importing and using libraries (requests, json), Working with Numpy and Pandas; Modular coding session, Reuse logic from one file in another, Build a simple calculator CLI, Calling a public API, Working with Data

AI vs ML vs DL, Supervised & Unsupervised learning, Neural networks intro, Reinforcement Learning basics, Search and optimization in AI agents; Quick quiz/discussion on ML types using case examples, Demonstration of basic models using visual tools (e.g., classification agent), Show DL in Real life with pretrained model examples

What are LLMs?, Transformer architecture (overview), Tokenization & Embeddings, Context windows & memory limits, Basics of prompt engineering; Tokenization demo (breaking inputs into tokens), Visualization: How attention works in Transformers, Live exploration: Prompt responses using various LLMs, Prompt tuning challenge: Generate summaries with context constraints

What are embeddings?, Token vectors vs sentence embeddings, Distance metrics (cosine, Euclidean), Intro to similarity search; Visualize word/sentence embeddings using dimension reduction (PCA/t-SNE), Explore cosine similarity between query and docs, Build basic vector search using OpenAI embeddings and FAISS

Basics of LangChain, OpenAI API, Prompt formatting and response parsing; Demo: Build a simple toolusing agent (e.g., use calculator + search tool), Prompt templates introduction

What is Agentic AI?, Autonomous AI vs Traditional AI,  Agent lifecycle and capabilities, Types of autonomy in AI systems, Realworld examples of agents (assistants, planners, scouts);  Discuss maturity levels of AI autonomy, Compare rulebased systems vs agents, Interactive brainstorming: Where can agents be used in business?, Live demo of a simple agent in action (e.g., task planner)

Agents vs Functions vs APIs, Overview of agentic libraries: LangChain, Autogen, CrewAI, Tool abstraction and orchestration basics, How agents use external tools via APIs; Walkthrough: Building a basic toolusing agent, Demo: Visual orchestration with LangFlow or Flowise, Agent execution tracing and debugging demo, Mini handson: Connecting a calculator or search tool

Types of agents: Reactive, Deliberative, Hybrid, Multiagent system fundamentals, Taskoriented vs Goaloriented agents, Agent communication & reasoning frameworks;  Diagramming session: Design your own agent architecture; Demo: CrewAI or Autogen multiagent setup; Simulation: Agent collaboration on a task (e.g., writer + editor agent); Agent design roleplay: Who does what in a team?

Goalsetting and planning in autonomous agents, Hierarchical vs reactive planning, Multiagent strategies, Reinforcement loops in planning; Live demo: Agent planning task using CrewAI or LangChain planning agent; Handson: Modify plan based on changing goals; Group activity: Define agent goals and break into tasks; Scenario simulation: Travel planning agent with constraints

Shortterm vs Longterm memory in agents, Vector stores and embedding storage, Semantic similarity & chunking, RetrievalAugmented Memory loop; Demo: Building an agent with memory (shortterm + longterm); Handson: Document chunking + vector search; Workshop: Improve agent response with memory access; Case Study: Memory use in customer support agents, MCP DEMO

Prompt types: Zeroshot, Oneshot, Fewshot; Chainofthought prompting; Instruction tuning basics; Dynamic prompt construction patterns; Prompt engineering lab: Refine prompts for clarity, tone, reasoning; Demo: Chainofthought & stepbystep tasks; Handson: Modular prompt assembly using LangChain or Flowise; Activity: Fix a badly behaving agent by prompt rewrites

Reinforcement Learning fundamentals, RLHF (Reinforcement Learning with Human Feedback), Reward systems for agents, Adaptive behavior tuning; Demo: Adaptive agent adjusting to new inputs (using OpenAI functions or simulated RL); Walkthrough: Feedback loops in task agents; Group activity: Design a reward system for a learning agent; Comparison: Hardcoded vs adaptive behaviors

RAG architecture overview, Combining retrieval and generation flows, Contextual embeddings & relevance ranking, Customizing RAG for Q&A, summarization;  Demo: Build a document Q&A bot with RAG; Handson: Connect embedding model + vector store + LLM chain; RAG tuning exercise: Improve relevance, reduce hallucination; Group debugging: Why is this RAG agent failing?

Hosting options for agents (cloud, serverless, embedded), API deployment walkthrough, Latency optimization basics, Monitoring principles; Demo: Deploying an agent on Hugging Face, Streamlit, or LangChain + FastAPI; Performance testing: latency & response quality; Add observability using LangSmith or OpenTelemetry; Live deployment of one agent endtoend

Why agent evaluation is hard, Key metrics: task success, coherence, correctness, coverage, latency, Logging & tracing basics, Manual vs automated evaluation approaches; Demo: Agent tracing and error inspection using LangSmith; Case review: Diagnosing agent failure (RAG or Planner); Handson: Design a custom evaluation rubric; Activity: Fix a broken agent based on logs

What is responsible AI?, Risks with autonomous agents (bias, hallucinations, misuse), Data privacy and consent for agent interactions; Group debate: Should AI agents make decisions independently; Walkthrough: Designing a safety layer (rate limiting, content filtering); Review real-world agent failures and ethical breaches

Agent use cases across domains (business, education, healthcare, customer support, research), ROI of agent adoption, Success stories and failure lessons; Demo: Business workflow automation agent (e.g., lead qualifier or research assistant); Group discussion: Analyze case studies; Ideation: Brainstorm your domainspecific agent; Interactive Q&A with instructor

What are lowcode/nocode tools for agents?, Pros and cons of visual pipelines, Intro to Flowise, LangFlow, AutoGen Studio, When to use lowcode over code"; Guided walkthrough: Build a multi-tool agent in Flowise or LangFlow; Explore visual node linking, memory blocks, and prompt templates; Live debugging with flow-based tools

Project guidelines and success checklist, Project templates: research agent, planner, assistant, tutor, etc., Tips on deployment & UI wrapping; Project planning and peer idea reviews; Demo: Stepbystep agent build with tools and memory; Breakout rooms for mentorship support; Final presentations & feedback; Group reflection: what worked, what didn’t; Instructor showcase of top submissions