Building Ai Agents: Core Component/ Intelligent Architecture

Core Components and Intelligent Architectures

What you'll learn

Understand the core components of AI agents and their architectures.

Build agents with sensors, effectors, memory, and decision-making engines.

Use tools and frameworks like LangChain, CrewAI, and AutoGen to create agents.

Design, test, and deploy a personalized AI agent as a final project.

Requirements

No prerequisites required — just curiosity and interest in AI. Basic programming knowledge is helpful but not mandatory.

Description

Building AI Agents: Core Components and Intelligent ArchitecturesArtificial Intelligence agents are no longer futuristic concepts — they are already powering chatbots, virtual assistants, trading bots, autonomous vehicles, and countless business applications. But what makes an AI agent truly effective? How do we design intelligent systems that can perceive, reason, act, and adapt in the real world?This hands-on course gives you a complete roadmap to understanding and building AI agents from the ground up. You’ll explore the core components of agent architecture — sensors, effectors, decision-making engines, knowledge bases, and communication interfaces — and learn how these pieces fit together into scalable, intelligent systems.Through step-by-step lessons, you’ll discover:The different types of agents (reactive, deliberative, hybrid) and their use casesHow agents perceive the world through text, images, audio, and APIsHow effectors enable agents to take meaningful actions in both digital and physical environmentsThe role of reasoning, planning, and memory in decision-makingHow to structure a knowledge base with databases, vector stores, and context cachingWays agents communicate with humans, systems, and other agentsTools and frameworks like LangChain, CrewAI, and AutoGen that accelerate developmentHow to add error handling and safety layers to keep agents reliable and trustworthyBy the end of this course, you will not only understand the anatomy of intelligent agents, but also gain the skills to design, extend, and deploy your own personalized AI agent as a final project.Whether you are a software developer, ML engineer, or AI enthusiast, this course will equip you with the knowledge and practical experience to build the next generation of intelligent AI systems.

Overview

Section 1: Introduction

Lecture 1 Download Course Materials

Lecture 2 What are AI agents?

Lecture 3 Real-World Applications of AI Agents

Lecture 4 Overview of agent architecture and what learners will build

Section 2: Core Concepts of AI Agents

Lecture 5 Definition and types of agents

Lecture 6 Agent lifecycle and core design principles

Lecture 7 Foundational elements

Section 3: Sensors – Perception Layer

Lecture 8 Types of sensors

Lecture 9 Preprocessing and interpreting sensor data

Lecture 10 Simulated vs Real-Time Perception

Section 4: Effectors – Action Layer

Lecture 11 What are effectors and how do agents take actions?

Lecture 12 Digital Effectors – Acting in Software Environments

Lecture 13 Feedback mechanisms

Section 5: The Decision-Making Engine

Lecture 14 Rule-based vs ML-based decision logic

Lecture 15 Incorporating LLMs for reasoning and planning

Lecture 16 Multi-step decision chains and memory use

Section 6: The Knowledge Base

Lecture 17 Structuring agent memory (short-term vs long-term)

Lecture 18 Using vector stores, databases, and context caching

Lecture 19 Dynamic Knowledge Updates & Queries

Section 7: Communication Interface

Lecture 20 How Agents Communicate

Lecture 21 Communication Channels for Agents

Lecture 22 Contextual Conversation Management

Section 8: Putting It All Together

Lecture 23 Architecting Modular, Extensible AI Agents

Lecture 24 Tools & Frameworks for Building Agents

Lecture 25 Error Handling & Safety Layers in Agents

Software developers interested in building intelligent AI systems,Machine learning engineers exploring agent architectures,Data scientists who want to integrate AI agents into workflows,AI enthusiasts eager to understand how agents work in practice