A Practical Guide to Building Agents

Learn how to design, develop, and deploy AI agents that can transform workflows and solve complex problems.

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Introduction

Large language models are becoming increasingly capable of handling complex, multi-step tasks. Advances in reasoning, multimodality, and tool use have unlocked a new category of LLM-powered systems known as agents.

This guide is designed for product and engineering teams exploring how to build their first agents, distilling insights from numerous customer deployments into practical and actionable best practices.

What are you looking to learn?

Take this quick quiz to help us direct you to the most relevant sections:

What is your current level of familiarity with AI agents?

  • I'm completely new to AI agents
  • I understand the basics but want to learn more
  • I have some experience building agents

What aspect of agents are you most interested in?

  • Understanding what agents are and when to use them
  • Learning about agent design and architecture
  • Implementing agents in my projects
  • Ensuring agent safety and reliability

Guide Overview

What is an Agent?

Understand the fundamental concepts of AI agents and how they differ from conventional software.

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When to Build an Agent?

Discover the scenarios where agents can add the most value to your workflows and processes.

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Agent Design Foundations

Explore the key components and architecture of effective agent systems.

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Guardrails

Learn how to implement safety measures and ensure your agents operate reliably.

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Agent Architecture

At its most fundamental form, an agent consists of three key components working together:

Agent Architecture Diagram showing Tool, Action, and Reasoning layers
Tool Layer +

The foundation of the system. This layer interfaces with external data sources and services, including APIs, vector databases, operational data, knowledge bases, and user interactions. It's responsible for fetching the raw information the system relies on.

Action/Orchestration Layer +

This layer is responsible for brokering the interactions between the LLM and the outside world (the tools). It handles interactions with the user, when applicable. It receives instructions from the LLM about which action to take next, performs that action, then provides the result to the LLM in the reasoning layer.

Reasoning Layer +

The core of the system's intelligence. This layer processes the retrieved information using a large language model (LLM). It determines what the agent needs to do next, leveraging context, logic, and predefined goals.

Track Your Progress

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Complete all sections to master the fundamentals of building AI agents!