Description

Introduction

The evolution of large language models (LLMs) has ushered in a new era of autonomous AI agents capable of decision-making, task execution, and collaboration. This course explores how these agents operate in real-world scenarios, focusing on the practical use of tools, frameworks, and design patterns. Learners will gain hands-on experience with the most widely used agentic AI frameworks, including LangChain, AutoGen, CrewAI, and others—alongside proven patterns for autonomy, tool use, memory, and collaboration.

Prerequisites

This course is suitable for developers, AI engineers, and technical leads with:

• Intermediate proficiency in Python

• Experience with LLMs (e.g., OpenAI GPT-4, Claude, or Gemini APIs)

• Familiarity with prompt engineering and API workflows

• (Optional) Exposure to LangChain, AutoGen, or related frameworks

Table of Contents

1. Introduction to Autonomous Agents

• 1.1 From Prompt Engineering to Agentic AI

• 1.2 Anatomy of an Autonomous Agent

• 1.3 Tasks Suited for Autonomous Execution

2. Tools and Capabilities of Agents

• 2.1 Tool Abstraction: What Are Tools in Agentic AI?

• 2.2 Integrating External Tools: Search, Web Scraping, Code Execution

• 2.3 Observability and Tool Use Patterns

3. Agentic Frameworks Overview

• 3.1 LangChain Agents and LCEL

• 3.2 AutoGen Agents and GroupChat

• 3.3 CrewAI: Role-based Agent Workflows

• 3.4 Other Notable Frameworks: MetaGPT, BabyAGI, OpenAgents

4. Patterns in Autonomous Agent Design

• 4.1 ReAct Pattern (Reasoning + Acting)

• 4.2 Plan-and-Execute Pattern

• 4.3 Multi-Agent Collaboration Pattern

• 4.4 Reflection and Self-Correction Loops

5. Building Real-World Autonomous Workflows

• 5.1 Automating Research and Report Generation

• 5.2 Task Delegation Among Multiple Agents

• 5.3 Coding Agents: Python Scripting, API Chaining, Data Analysis

• 5.4 Integration with External APIs and Services

6. Memory, Context, and Continuity

• 6.1 Short-Term vs Long-Term Memory in Agents

• 6.2 Vector Stores and Embedding-based Recall

• 6.3 Dialogue Continuity and Agent State

7. Evaluation, Safety, and Monitoring

• 7.1 Debugging Agent Behavior

• 7.2 Fail-safes, Guardrails, and Human-in-the-Loop

• 7.3 Logging, Metrics, and Performance Tuning

8. Future of Autonomous Agents

• 8.1 Scaling Agents in Enterprise Environments

• 8.2 Agent App Architectures and Standardization (e.g., Open Agents Protocol)

• 8.3 Open Challenges and Research Directions

Autonomous agents are redefining the landscape of intelligent automation. With the right tools, frameworks, and patterns, developers can build systems that act independently, solve complex problems, and adapt over time. This course bridges theory and practice, offering a launchpad into real-world Agentic AI development. You will leave with the confidence and skillset to implement autonomous agents in production workflows.