The Complete Class for Agentic AI Design Patterns

Name: The Complete Class for Agentic AI Design Patterns
Rating: 4.4 (506 reviews)

Learn everything about Agentic AI Design Patterns in this course and Learn different patterns and Many More!

Created byMani Kuda

Last updated 8/2026

English

English [Auto],

What you'll learn

You will learn different types of design patterns in Agentic AI Design Patterns
You will learn some key Technologies in Agentic AI Design Patterns
You will learn behavioral Patterns and Coordination Patterns in Agentic AI Design Patterns
You will learn interaction Patterns and Agentic Designs pattern

Course content

80 sections • 403 lectures • 101h 48m total length

Introduction6:24
Explore agentic design patterns for intelligent, scalable multi-agent systems and learn to apply frameworks like Jade and Spade. See real-world case studies of agents transforming cities and autonomous vehicles.

Getting Started With VS Code10:36
Explore key technologies shaping the future of agentic design patterns, including explainable AI, blockchain, edge computing, federated learning, reinforcement learning, and cognitive multi-agent systems with IoT.
Python Basics-Syntax And Semantics20:17
Explore Lang Graph, a Lang Chain framework for stateful multi-agent workflows with LLMs, enabling looping, branching, human-in-the-loop, and real-time streaming, with deployment and monitoring via LAN cloud.
Variables In Python18:55
Create a LangGraph state graph by adding agent and tool nodes and defining transitions. Use conditional edges to control workflow continuation and enable memory with a checkpoint.
Basics DataTypes In Python9:52
Discover how behavioral patterns define agent responses to stimuli, enabling modular, reusable, and decoupled behavior through observer and strategy patterns in multi-agent systems.
Operators In Python16:16
Explore coordination patterns that drive dynamic task and resource allocation among agents, focusing on contract net protocol and auction protocols, and how bidding determines task assignment and execution.

Conditional Statements in Python21:03
Explore mediator and chain of responsibility patterns for agentic ai design, highlighting centralized coordination, decoupled agents, task delegation, and scalability among interacting agents.
Loops In Python28:02
Explore learning patterns that enable agents to learn from feedback, test actions through exploration, and optimize strategies in dynamic environments using reinforcement learning and evolutionary algorithms.

Lists In Python37:07
Explore negotiation patterns that define structured approaches to conflict resolution and resource sharing among agents. Compare bargaining models and multi-agent strategies, highlighting iteration, utility maximization, coalition formation, and dynamic adaptation.
Tuples In Python22:34
Explore structural patterns for agentic systems, including the composite pattern for hierarchical agents and the proxy pattern for secure access, improving scalability and resource management.
Dictionaries In Python38:18
Real World Use cases Of List9:50

Classes And Objects In Python22:55
Single And Multiple Inheritance19:00
Polymorphism In OOPS19:08
Encapulation In OOPS22:12
Abstraction In OOPS9:08
Magic Methods In Python8:03
Operator Overloading In Python8:31
Custom Exception Handling7:05
Iterators In Python6:25
Generators In Python11:06
Fucntion Copy.Closures and Decorators21:15
Numpy In Python28:16
Pandas-DataFrame And Series29:09
Data Manipulation With Pandas And Numpy24:38
Reading Data From Various Data Source Using Pandas15:04
Logging Practical Implementation In Python14:37
Logging With Multiple Loggers4:40
Logging With A Real World Examples7:43
Introduction To Pydantic14:07
Pydantic Practical Implementation21:38

Getting Started With Langchain And Open AI11:08
Creating Virtual Environment7:12
Important Components Of LangChain13:09
Data Ingestion With Documents Loaders22:43
Recursive Character Text Splitter12:56
Character Text Splitter With Langchain3:53
HTML Header Text Splitter7:01
Recursive Json Text Splitter6:48
Introduction To OPENAI Embeddings22:20
Ollama Embeddings15:43
HuggingFace Embeddings10:20
Vector Stores-FAISS16:34
Vector Store And Retriever- Chroma DB9:32

Requirements

Need to have computer and internet

Description

You will learn Agentic AI Design Patterns in this course. The Agentic AI refers to artificial intelligence systems designed to operate autonomously, make decisions, and achieve specific objectives. Unlike traditional AI models that follow predefined rules, agentic AI systems exhibit goal-oriented behavior, learning, adapting, and interacting dynamically with their environment. To build such intelligent agents effectively, developers use structured approaches known as agentic AI design patterns. These patterns help create AI systems that are scalable, efficient, and capable of reasoning independently.

A crucial aspect of agentic AI is learning and adaptation, where agents improve their performance over time. Machine learning techniques, such as reinforcement learning and continual learning, enable AI to refine its strategies based on past experiences. This pattern is essential in applications like fraud detection, personalized recommendations, and adaptive AI tutors. When multiple AI agents interact, the multi-agent collaboration pattern comes into play. This approach enables multiple intelligent systems to coordinate their actions to achieve a shared objective, commonly seen in swarm robotics, distributed AI, and supply chain automation.

Agentic AI design patterns provide a structured framework for developing intelligent, autonomous systems. By leveraging goal-driven behavior, hierarchical decision-making, adaptability, and collaboration, AI agents can solve complex problems effectively while ensuring safety and ethical compliance.

Who this course is for:

Anyone wanting to learn Agentic AI Design Patterns

The Complete Class for Agentic AI Design Patterns

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 6min

Basics of Python for Agentic AI and Generative AI5 lectures • 1hr 16min

Python Control Flow for Agentic AI and Generative AI2 lectures • 49min

Data Structures Using Python for Agentic AI and Generative AI4 lectures • 1hr 48min

Functions In Python for Agentic AI and Generative AI5 lectures • 1hr 22min

Importing, Creating Modules in Python for Agentic AI and Generative AI2 lectures • 35min

File Handling In Python for Agentic AI and Generative AI3 lectures • 51min

OOPS Classes And Objects for Agentic AI and Generative AI20 lectures • 5hr 15min

Langchain Hands On13 lectures • 2hr 39min

Getting Started With OpenAI And Ollama6 lectures • 1hr 21min

Requirements

Description

Who this course is for: