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Advanced Agentic AI Development
This advanced Agentic AI course is designed to teach cutting-edge techniques for building autonomous AI agents and intelligent systems. You will learn how to design, deploy, and optimize AI-driven workflows using modern frameworks and real-world applications to create scalable, efficient, and innovative solutions.
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Agentic AI – Build Smarter, Automate Intelligently
Our Advanced Agentic AI program is designed to help learners master the development of intelligent, autonomous AI agents using powerful modern frameworks. This course focuses on designing real-world workflows, enhancing decision-making capabilities, and applying advanced AI techniques to build scalable, efficient, and industry-ready solutions for automation and business transformation.
Duration
6 Months
Sessions
66
Classes Days
Mon, Wed, Fri
Summary Of The Course
This course is designed for absolute beginners with no prior programming experience and aims to transform them into job-ready AI engineers. It covers foundational programming, backend development, databases, and advanced AI systems including RAG and agentic AI. The curriculum emphasizes practical implementation, system design thinking, and real-world project development to prepare students for freelance work and industry roles.
- Programming & Problem Solving (Python)
- Backend Development & APIs (FastAPI)
- Databases & Data Handling (PostgreSQL/MySQL)
- AI & LLM Fundamentals
- Prompt Engineering & AI Integration
- RAG Systems (Vector Databases – Qdrant)
- Agentic AI & Multi-Agent Systems
- Deployment & Production Systems
- Freelancing & Job Readiness
After completing this course, students will be able to:
- Build complete AI-powered applications from scratch
- Develop backend systems integrated with databases
- Design and deploy RAG-based AI systems
- Build intelligent agents with tools, memory, and workflows
- Create portfolio-ready projects for freelance or job opportunities
📚 Lectures
Programming Foundations (Classes 1–8)
Subjects Discussed
1. Introduction to Programming & AI Landscape
2. Variables, Data Types, and Input/Output
3. Conditions & Decision Making
4. Loops (for, while) & Iteration Thinking
5. Functions & Code Modularity
6. Lists, Dictionaries, and Data Structures
7. Problem Solving & Debugging
8. Mini Project (CLI-Based Application)
Problem Solving & Data Handling (Classes 9–14)
Subjects Discussed
10. Debugging & Error Handling
11. File Handling (TXT, CSV)
12. JSON & Real-World Data Formats
13. Introduction to Pandas
14. Mini Project (Data Analysis & Report Generator)
Backend Development (Classes 15–24)
Subjects Discussed
16. FastAPI Setup & Project Structure
17. GET APIs & Routing
18. POST APIs & Input Handling
19. Pydantic Validation
20. Error Handling & API Design
21. Authentication Basics
22. Build: Task Manager API
23. Build: User Management System
24. Project Review & Optimization
Databases & Integration (Classes 25–30)
Subjects Discussed
25. Database Fundamentals & SQL Basics
26. Advanced SQL (Joins, Filtering, Aggregations)
27. PostgreSQL/MySQL Setup
28. ORM (SQLModel)
29. API + Database Integration
30. Mini Project (Job Board Backend System)
AI & LLM Fundamentals (Classes 31–36)
Subjects Discussed
31. Introduction to AI, ML, and LLMs
32. How LLMs Work (Tokens, Context)
33. Limitations & Use Cases of LLMs
34. LLM APIs (Ollama/OpenAI)
35. Build: Basic Chatbot (Guided)
36. Analysis & Improvement of AI Outputs
Prompt Engineering & AI Integration (Classes 37–44)
Subjects Discussed
37. Prompt Structure & Design
38. System vs User Prompts
39. Few-shot Prompting Techniques
40. Output Control & Formatting
41. API Integration for AI Applications
42. Streaming Responses & Error Handling
43. Build: AI Content Generator
44. Build: AI Resume Generator
RAG Systems (Classes 45–52)
Subjects Discussed
45. Introduction to RAG (Why it matters)
46. Embeddings (Concept + Usage)
47. Vector Databases (Qdrant)
48. Chunking & Retrieval Strategies
49. Build: PDF Chatbot (Part 1)
50. Build: PDF Chatbot (Part 2)
51. Optimization & Performance Tuning
52. Project Review & Improvements
Agentic AI Systems (Classes 53–62)
Subjects Discussed
53. Introduction to AI Agents & Architectures
54. Tool Usage & Tool Calling
55. Prompting for Agent Behavior
56. Memory Systems (Short-term & Long-term)
57. Planning & Task Decomposition
58. Multi-Agent Systems & Role Design
59. Orchestration with LangGraph
60. Agent + RAG Integration
61. Build: AI Automation Agent (Part 1)
62. Build: AI Automation Agent (Part 2)
Deployment & Production (Classes 63–66)
Subjects Discussed
63. Deployment Fundamentals (Local & Cloud)
64. Docker Basics for AI Applications
65. API Deployment & Hosting
66. Monitoring, Logging & System Reliability
Why Learn Agentic AI Systems?
Master next-generation autonomous AI systems, learn how to design and deploy intelligent agents, and build solutions that can plan, reason, and execute tasks independently to solve real-world business and technical problems with high impact.
Better Career Opportunities
Gain in-demand Agentic AI skills for roles in AI, automation, and intelligent system development.
Freelancing & Online Income
Build AI agents for global clients and earn online with automation solutions.
Business & Brand Growth
Use Agentic AI to automate operations and scale your business efficiently.
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