Modern Automated AI Agents: Building Agentic AI to Perform Complex Tasks

5+ Hours of Video Instruction

Get started with automated AI agents

Overview

Modern Automated AI Agents introduces you to the concept of automated agents. It then helps you build a solid understanding of how to design, build, and optimize AI agents to tackle real-world challenges.

About the Instructor

Sinan Ozdemir is the founder and CTO of LoopGenius, where he uses state-of-the-art AI to help people create and run their businesses. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks and videos on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, California.

• Build & use AI agents
• Evaluate AI agent frameworks
• Start to use CrewAI
• Design multi-step workflows with LangGraph
• Use large language models (LLMs)
• Integrate existing and custom tools
• Use thought, action, observation, and response components
• Test and evaluate agents, their responses, backstories, definitions, and rules
• Add planning and reflection to agents to bolster performance

Who Should Take This Course

Developers, data scientists, and engineers who are interested in building intelligent, autonomous AI agents capable of solving complex problems and adapting to dynamic environments

Course Requirements

• 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

Lesson Descriptions

Lesson 1: Introduction to AI Agents

Lesson 1 explores the components of a modern AI agent, their core components, and how they differ from the LLMs under the hood. You learn how agents automate tasks, make decisions, and adapt to dynamic environments to deliver impactful results.

Lesson 2: Under the Hood of AI Agents

Lesson 2 dives into the mechanics of AI agents, exploring the different types of LLMs and how one type in particular of LLM, the autoregressive model, powers virtually all agent workflows. You gain insight into how tools, prompts, and agent contexts work together to create intelligent AI agent systems.

Lesson 3: Building an AI Agent

In Lesson 3 it is time to put theory into practice by designing and building your own fully functional AI agent framework. You integrate tools, construct a viable prompt, and learn to handle user inputs dynamically to create adaptable end-to-end agentic systems.

Lesson 4: Testing and Evaluating Agents

Lesson 4 focuses on measuring agent performance using simple metrics such as accuracy, response time, and task completion rates before graduating to more advanced strategies for addressing structural agentic biases and ensuring reliable outcomes of generative agent responses.

Lesson 5: Expanding on ReAct with Planning and Reflection

In this lesson, we will enhance our own agents with planning and reflection techniques, allowing them to reason through tasks with a bit more care. These methods will enable our agents to handle even simple workflows with more scrutiny and generally result in a higher quality output.

Lesson 6: Advanced Applications and Future Directions

In the previous lessons you've built agents, iterated on them, tested them, but what's next? Lesson 6 covers emerging trends like meaningful multi-agent collaboration, real-time data integration, and ethical considerations of automating certain types of work.

About Pearson Video Training

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