Introduction to AI Agents

Introduction to AI Agents

Duration: 2 Days

Description

AI agents extend language models with the ability to reason through tasks, use tools, and take actions toward a goal. This course introduces developers to agent concepts and shows how to build simple, reliable agents. Participants learn how agents decide what to do, how tool use and function calling work, and how to keep agents focused and safe. Through hands-on exercises, attendees build working agents and gain the foundation for more advanced agent frameworks and patterns.
Class time is divided between instructor-led explanation (40 percent) and hands-on development (60 percent).

Audience

Developers who can build LLM-powered features and want to understand and create AI agents. A strong starting point before working with agent frameworks.

Objectives

  • Explain what AI agents are and how they differ from simple LLM calls
  • Describe the reason-act loop and how agents make decisions
  • Implement tool use and function calling
  • Give agents access to data and external actions safely
  • Keep agents reliable, bounded, and observable
  • Build a simple working agent

Prerequisites

Participants should be comfortable building basic LLM features. The following will help:

  • Working knowledge of Python (see Python Foundations)
  • Experience calling AI model APIs (see AI for Developers)
  • Familiarity with LLM application patterns (see Building LLM Applications)

Course Outline

  • Module 1 – Course Introduction
    • Welcome and objectives
    • What is an AI agent?
    • Agents versus chatbots versus workflows
  • Module 2 – How Agents Work
    • The reason-act loop
    • Planning and decision-making
    • Memory and context for agents
    • Where agents succeed and where they struggle
  • Module 3 – Tools and Function Calling
    • Giving agents tools
    • Function calling fundamentals
    • Designing good tool interfaces
    • Hands-on exercise: an agent that uses a tool
  • Module 4 – Data and Actions
    • Connecting agents to data sources
    • Taking real-world actions safely
    • Permissions and guardrails
    • Confirming and validating agent actions
  • Module 5 – Reliability and Observability
    • Keeping agents on task
    • Handling failures and loops
    • Logging and tracing agent behavior
    • Cost and latency considerations
  • Module 6 – Building an Agent
    • Designing a simple agent end to end
    • Hands-on exercise: build and test an agent
  • Module 7 – Course Wrap-Up
    • Key takeaways
    • Resources for continued learning
    • Next steps toward advanced agents