Oreilly – Modern Automated AI Agents: Building Agentic AI to Perform Complex Tasks 2025-1
Oreilly – Modern Automated AI Agents: Building Agentic AI to Perform Complex Tasks 2025-1
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Modern Automated AI Agents: Building Agentic AI to Perform Complex Tasks. This course introduces you to the concept of automated agents and helps you gain a comprehensive understanding of designing, building, and optimizing AI agents to solve real-world challenges.
What you will learn:
- Building and using artificial intelligence agents
- Evaluating AI Agent Frameworks
- Getting started with CrewAI
- Designing multi-step workflows with LangGraph
- Using Large Language Models (LLMs)
- Integrate existing and custom tools
- Using the components of thought, action, observation, and response
- Testing and evaluating agents, responses, background, definitions, and their rules
- Adding planning and reflection to agents to enhance performance
This course is suitable for people who:
- Developers, data scientists, and engineers interested in building intelligent, autonomous AI agents that are capable of solving complex problems and adapting to dynamic environments.
Description of the Modern Automated AI Agents course: Building Agentic AI to Perform Complex Tasks
- Publisher: Oreilly
- Instructor: Sinan Ozdemir
- Training level: Beginner to advanced
- Training duration: 5 hours and 37 minutes
Course topics
- Introduction
- Modern Automated AI Agents: Introduction
- Lesson 1: Introduction to AI Agents
- Topics
- 1.1 Overview of AI Agents and Their Applications
- 1.2 Leading AI Agent Frameworks
- 1.3 First Steps with Agents with CrewAI
- 1.4 Designing Multi-Step Workflows with LangGraph
- Lesson 2: Under the Hood of AI Agents
- Topics
- 2.1 Understanding Large Language Models (LLMs)
- 2.2 Introduction to Tool Integration
- 2.3 Key Agent Components: Thought, Action, Observation, Response
- Lesson 3: Building an AI Agent
- Topics
- 3.1 Building Custom Tools
- 3.2 Building Our Agent Prompt
- 3.3 Using Our Agent
- Lesson 4: Testing and Evaluating Agents
- Topics
- 4.1 How to Evaluate Agents
- 4.2 Evaluating Tool Selection and Use
- 4.3 Assessing the Quality of Agent Responses
- 4.4 Evaluating Agent Backstories, Task Definitions, and Rules
- Lesson 5: Expanding on ReAct with Planning and Reflection
- Topics
- 5.1 Why Agents Fail
- 5.2 Plan and Execute Agents
- 5.3 Reflection Agents
- Lesson 6: Advanced Applications and Future Directions
- Topics
- 6.1 Exploring Additional Tools and APIs
- 6.2 Future Trends in AI Agents
- Summary
- Modern Automated AI Agents: Summary
Course prerequisites
- Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels)
- Comfortable using the Pandas library and either Tensorflow or PyTorch
- Understanding of ML/deep learning fundamentals including train/test splits, loss/cost functions, and gradient descent
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 720p
Download link
File(s) password: www.downloadly.ir
File size
1.6 GB
