What you will learn?
Module 1: Foundations of Agentic AI
Module 2: Building AI Agents
Module 3: Advanced Agent Engineering
Module 4: Real-World AI Projects
Module 5: Capstone Project
About this course
Build the Next Generation of Intelligent AI Systems
Master the future of AI by learning how to design autonomous, tool-enabled, context-aware AI applications using modern technologies like Claude, OpenAI, APIs, RAG pipelines, and Multi-Agent Architectures.This intensive hands-on internship is designed to give students real-world experience in building production-ready AI workflows and intelligent automation systems.
Build Intelligent Agents. Integrate APIs. Create Production-Ready AI Solutions.
WHY JOIN THIS PROGRAM?
🚀 Hands-On Learning
🧠Industry-Focused Curriculum
âš¡ Real-World AI Projects
🔥 Future-Ready Skills
COURSE OBJECTIVES
- During this internship, you will learn to
- Understand the fundamentals of Agentic AI Systems
- Master Prompt Engineering techniques for advanced AI interaction
- Build AI Agents using Claude & OpenAI models
- Integrate APIs, external tools, and automation workflows
- Design and orchestrate Multi-Agent Systems
- Implement RAG (Retrieval-Augmented Generation) applications
- Create autonomous AI workflows and assistants
- Develop and deploy a real-world AI project
FAQ
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1. Evolution of Artificial Intelligence
2. Traditional AI vs Generative AI
3. Understanding Large Language Models (LLMs)
4. Modern AI ecosystem overview
1. What are AI Agents?
2. Chatbots vs AI Assistants vs AI Agents
3. Components of Agentic Systems
4. Autonomous workflows fundamentals
1. Claude ecosystem overview
2. OpenAI ecosystem overview
3. Context window comparison
4. Use-case driven model selection
1. Zero-Shot & Few-Shot Prompting
2. Role-Based Prompting
3. Chain-of-Thought Reasoning
4. Structured & Context-Aware Prompts
1. Goal Definition
2. Planning
3. Reasoning
4. Action Execution
5. Reflection & Optimization
1. Why Agents Need Tools
2. Tool Calling Concepts
3. Function Calling in LLMs
4. External Tool Integration
1. API Fundamentals
2. REST APIs Basics
3. GET / POST Requests
4. Authentication & Tokens
5. Working with AI APIs
1. Short-Term Memory
2. Long-Term Memory
3. Conversation History Management
4. Context Window Optimization
1. Designing Single-Purpose Agents
2. Planner → Executor Workflow
3. Agent Decision Making
4. Building a Working AI Agent
1. Research Assistant Agent
2. Task Automation Agent
3. Smart Question Answering Agent
1. RAG Architecture Overview
2. Embeddings & Vector Search
3. Knowledge Retrieval Pipeline
4. Document-Aware AI Systems
1. Multi-Agent Design Principles
2. Planner Agent
3. Research Agent
4. Reviewer Agent
5. Collaborative Agent Communication
1. Chains vs Agents
2. Workflow Orchestration
3. State Management
4. Building Agent Pipelines
1. Context Engineering Concepts
2. Managing Large Context Windows
4. Knowledge Injection Strategies
5. MCP (Model Context Protocol) Basics
1.. Multi-Step Reasoning
2. Autonomous Decision Chains
3. Workflow Automation Scenarios
1. Web Research Automation
2. Data Collection
3. Summarization Pipelines
4. Report Generation
1. Code Generation
2. Debugging Assistance
3. Documentation Generation
4. Developer Productivity Workflows
1. Email Automation
2. Meeting Notes & Summaries
3. Workflow Automation Use Cases
4. Productivity Agents
1. PDF Processing
2. Information Extraction
3. Semantic Search
4. Intelligent Document Q&A
1. System Design Thinking
2. Tool Integration
3. Real-World Deployment Considerations
1. Project Ideation
2. Problem Statement Definition
3. Use-Case Selection
4. Requirement Gathering
1. Agent Architecture Planning
2. Workflow Mapping
3. Tool & API Selection
4. System Design Documentation
1. Building End-to-End Solution
2. Integration & Testing
3. Iterative Improvements
4. Debugging & Validation
1. Demo Flow Creation
2. Presentation Preparation
3. Technical Walkthrough
4. Solution Storytelling
1. Project Presentation
2. Live Working Demo
3. Evaluation & Feedback
4. Certification Completion
