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Starts 24 June 2025 10:33

Ends 24 June 2025

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Introduction to Developing AI Agents

Unlock the potential of AI by diving into the "Introduction to Developing AI Agents" course offered by Pluralsight. Whether you're new to the field or looking to deepen your understanding, this course provides you with the foundational knowledge and skills to start building AI agents. Begin with creating a basic framework in Python, and progr.
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Overview

AI agents are set to become a key driver in the evolution of artificial intelligence in the near term. AI agents are revolutionizing generative AI by leveraging LLMs as reasoning engines and using tools (function calls) to perform multistep tasks.

In this course, Introduction to Developing AI Agents, you’ll learn to create these AI agents. First, you’ll explore the fundamentals of AI agents, including their architecture, types, and use cases.

Next, you’ll implement a basic AI agent framework in Python. Finally, you’ll learn to develop advanced agents that leverage LLMs to reason, interact with its environment, and achieve goals.

By the end of this course, you’ll have the skills to build agentic AI Applications (with memory and tool-using capabilities) to automate workflows.

Syllabus

  • Course Introduction
  • Overview of AI agents
    Importance of AI agents in modern AI systems
    Course objectives and outcomes
  • Fundamentals of AI Agents
  • Definition and characteristics
    Types of AI agents (reactive, deliberative, interactive)
    Key components: Perception, Decision-Making, and Action
    Use cases and applications
  • AI Agent Architecture
  • Understanding agent environments
    Key architectural components (sensors, effectors, reasoning engines)
    Introduction to tools and function calls for task execution
  • Implementing Basic AI Agents
  • Setting up the development environment
    Building a simple AI agent in Python
    Testing agent functionality
  • Introduction to Large Language Models (LLMs) in AI Agents
  • Overview of LLMs and their capabilities
    Leveraging LLMs for reasoning tasks
    Integrating LLMs into basic agent framework
  • Advanced AI Agent Development
  • Designing agents with memory capabilities
    Building tool-using agents
    Case study: Multi-step task automation with AI agents
  • Developing Agentic AI Applications
  • Understanding environmental interaction
    Strategies for achieving complex goals
    Real-world examples and applications
  • Course Project
  • Developing a functional AI agent with memory and tool-use features
    Applying AI agent to automate a simple workflow
  • Conclusion and Next Steps
  • Recap of key learnings
    Future trends in AI agent development
    Additional resources and continued learning opportunities

Taught by

Muhammad Sajid


Subjects

Computer Science