Agentic AI BootCamp

• Introduction to Generative AI and Large Language Models
• Capabilities and Business Applications of GenAI
• Limitations and Challenges of Generative AI
• Introduction to Agentic AI Concepts and Architecture
• Evolution from GenAI to Autonomous Agentic Systems

Topics:

• What is an AI Agent?
• LLM as reasoning engines
• Planner, Executor, Tools, and Memory

Learning Objectives:

• Introduce the concept and components of AI agents.

Learning Outcomes:

• Understand the agent architecture and roles of each part.

Use Cases/Projects:

• Create a rule-based planning agent with memory.

Assessment Methods:

• Short quiz + code implementation.

Topics:

• Structure of a prompt, Advanced prompting technique like COT, TOT

Learning Objectives:

• Should be able to understand how to write prompts for generating desired output from LLM

Topics:

• AutoGPT, BabyAGI, LangGraph overview, Crew AI framework
• ReAct, Plan-and-Execute, Tree of Thought
• Goal decomposition and task queues

Learning Objectives:

• Explore agentic frameworks and architecture patterns.

Learning Outcomes:

• Able to choose and use frameworks like AutoGen and CrewAI.

Use Cases/Projects:

• Build a simple CrewAI or LangGraph agent pipeline.

Assessment Methods:

• Demo and code walkthrough.

Topics:

• Vector Databases: FAISS, Pinecone, Weaviate
• Embedding generation and similarity search
• RAG pipelines with LangChain and LlamaIndex

Learning Objectives:

• Implement knowledge-aware LLM applications using RAG.

Learning Outcomes:

• Able to connect vector DBs with LLMs to build RAG pipelines.

Use Cases/Projects:

• RAG Chatbot – Develop a knowledge-based chatbot with vector DB + LangChain

Assessment Methods:

• Pipeline performance evaluation + demo.

Topics:

• Crew AI framework, Autogen Framework , LangGraph Framework

Learning Objectives:

• Learn about different Frameworks

Learning Outcomes:

• Able to create use cases on different framework

Topics:

• API calling and browser tools
• File system interaction (read/write, parse)
• Function calling with OpenAI, Claude

Learning Objectives:

• Enable agents to interact with external tools and services.

Learning Outcomes:

• Agents can fetch, read, write, and manipulate data.

Use Cases/Projects:

• Build an agent that queries web APIs and updates documents.

Assessment Methods:

• Code demo and test scenarios.

Topics:

• Short-term vs long-term memory
• Vector memory with FAISS, Pinecone
• LangGraph & memory persistence

Learning Objectives:

• Implement agent memory for enhanced context.

Learning Outcomes:

• Create agents with persistent vector memory.

Use Cases/Projects:

• Create a chatbot with persistent user memory.

Assessment Methods:

• Functionality demonstration with memory storage.

Topics:

• Multi-step planning with ReAct
• Decision making under constraints
• Dynamic tool chaining

Learning Objectives:

• Enable agents to reason and plan across multiple steps.

Learning Outcomes:

• Agents can dynamically adapt plans and tool usage.

Use Cases/Projects:

• Build a goal-seeking assistant with dynamic steps.

Assessment Methods:

• Step-by-step task execution assessment.

Topics:

• CrewAI, AutoGen, CAMEL frameworks
• Role-based agent orchestration
• Message-passing and negotiation

Learning Objectives:

• Introduce agents that collaborate and negotiate.

Learning Outcomes:

• Design multi-agent systems with defined roles.

Use Cases/Projects:

• Create buyer-seller agents that simulate transactions.

Assessment Methods:

• Group demo with inter-agent communication.

Topics:

• Dockerizing and deploying agents
• Cloud infra (AWS Lambda, GCP, Azure, HuggingFace Spaces)
• LLMOps for Agents

Learning Objectives:

• Deploy agents in production-ready infrastructure.

Learning Outcomes:

• Deploy scalable and maintainable agent applications.

Use Cases/Projects:

• Deploy a LangGraph agent on HF Spaces with Docker.

Assessment Methods:

• Deployment report and live link submission.

Topics:

• Loop prevention and constraint design
• Goal alignment and ethical agents
• Trust boundaries and permission control

Learning Objectives:

• Ensure safe, ethical, and aligned agent behaviors.

Learning Outcomes:

• Able to implement safety constraints and ethical checks.

Use Cases/Projects:

• Implement constraints in a goal-chasing agent.

Assessment Methods:

• Checklist-based review + discussion.

Topics:

• Creating monetizable agent products
• Use case templates (travel planner, sales bot)
• Scheduling, persistent jobs, dashboards

Learning Objectives:

• Productize agents and offer as commercial services.

Learning Outcomes:

• Design agent SaaS apps for real-world use cases.

Use Cases/Projects:

• Build and price a travel planning SaaS agent.

Assessment Methods:

• Business model presentation + prototype demo.

Topics:

• Introduction to MCP (Multi-Agent Communication Protocols)
• Understanding A2A (Agent-to-Agent) Protocol
• Deep Dive into ACP (Agent Collaboration Protocol)
• Comparing MCP vs A2A vs ACP
• When and how to apply each protocol in real-world agent systems

Learning Objectives:

• Understand the core principles and differences between MCP, A2A, and ACP protocols.
• Learn when and how to apply each protocol in real-world multi-agent systems.
• Gain practical exposure to implementing these protocols using frameworks like AutoGen and CrewAI.

Learning Outcomes:

• Ability to design and implement agent communication using MCP, A2A, and ACP.
• Confidently choose the right protocol based on system requirements and agent behavior.
• Deliver functional multi-agent workflows with appropriate communication and collaboration logic.

• n8n overview, architecture, and core concepts
• Workflow creation using triggers, nodes, and executions
• Data handling with JSON, expressions, conditions, and errors
• Integrations with APIs, databases, and third-party tools
• Advanced workflows, AI agents, deployment, and best practices

Topics:

• Build a multi-agent system with memory, tools, reasoning
• Use CrewAI or LangGraph orchestration
• Real-world example: Autonomous CRM assistant

Learning Objectives:

• Demonstrate integrated agentic skills through a real-world project.

Learning Outcomes:

• Deliver a functional multi-agent system for practical application.

Use Cases/Projects:

• Choose a Capstone Project from your desired domain. Focuses on the end-to-end implementation of the whole course.
• Use Case 1: Go-to-Market Strategy for a New Energy Drink using Agentic AI

Objective:

• To effectively position and launch a new energy drink in a competitive market by leveraging autonomous agents for market intelligence and strategic planning.
• (or)
• Use Case 2: Multi-Agents AI for Product Launch Strategy

Objective: 

• A beverage company is preparing to launch a new energy drink. Success depends on understanding market trends, identifying consumer preferences, and creating a compelling product positioning strategy.

Assessment Methods:

• Peer-reviewed capstone submission and demo.

Subtopics:

Foundation & Personal Branding

• Career Vision & Mapping
• Resume Mastery
• LinkedIn Optimisation
• Portfolio & GitHub showcase

Job Market Readiness

• Job search strategy
• Mock Interviews (Behavioural)
• Mock Interviews (Technical)