Agentic AI with Python

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Agentic AI Course Overview:

Agentic AI Course is designed to train technical freshers and software developers to build AI-powered, autonomous agents using Python and modern AI frameworks. The program focuses on enabling learners to design, develop, and deploy AI agents within real-world software systems.
Students will gain hands-on experience in integrating Large Language Models (LLMs), designing agent workflows, and embedding AI agents into web applications to meet current and future industry demands.

Course Structure

• book_2 Module 1: Coding with Python

  • • Python syntax and programming fundamentals.
  • • Variables, data types, and control structures
  • • Functions and modular programming
  • • Data structures: lists, tuples, dictionaries, sets
  • • File handling and exception management
  • • Writing clean and maintainable Python code

• book_2 Module 2: AI & Machine Learning

  • • Object-Oriented Programming in Python
  • • Working with JSON and configuration files
  • • Consuming and exposing APIs using Python
  • • Virtual environments and package management
  • • Introduction to asynchronous programming concepts

• book_2 Module 3: Prompt Engineering & Large Language Models (LLMs)

  • • Introduction to Artificial Intelligence and Generative AI
  • • Understanding Large Language Models (LLMs)
  • • Tokens, context windows, and cost considerations
  • • Interacting with LLMs using Python
  • • Handling and validating AI-generated outputs

• book_2 Module 4: Prompt Engineering for Developers

  • • Principles of effective prompt design
  • • System, user, and assistant prompts
  • • Dynamic prompt creation using Python
  • • Structured outputs (JSON-based responses)
  • • Prompt optimization and error handling

• book_2 Module 5: Agentic AI Concepts and Architecture

  • • Introduction to Agentic AI systems
  • • Difference between chatbots and AI agents
  • • Agent lifecycle: planning, execution, observation, decision-making
  • • Tools, memory, and reasoning mechanisms
  • • Single-agent and multi-agent architectures

• book_2 Module 6: Building AI Agents Using Python

  • • Designing task-oriented AI agents
  • • Tool calling and function execution
  • • Integrating APIs and databases with agents
  • • Managing agent context and memory
  • • Developing custom agent logic

• book_2 Module 7: Agent Frameworks (Python-Based)

  • • Overview of Python agent frameworks
  • • Building agents using LangChain
  • • Multi-agent systems using CrewAI
  • • Framework selection and architectural considerations
  • • Best practices for scalable agent development

• book_2 Module 8: AI Agents with Backend Frameworks

  • • Developing AI agent services using FastAPI / Django
  • • Designing REST APIs for AI-powered applications
  • • Authentication and user-specific agent behavior
  • • Background processing and task orchestration
  • • Managing long-running AI workflows

• book_2 Module 9: Frontend Integration & Application Embedding

  • • Exposing AI agents through APIs
  • • Integrating agents into web applications
  • • Handling asynchronous responses
  • • Webhooks and agent triggers
  • • UI considerations for AI-driven features