A practical guide to building agents

"A Practical Guide to Building Agents", published by OpenAI, is aimed at product and engineering teams developing AI agents based on large language models (LLMs). These agents can autonomously perform complex tasks—such as resolving support cases or generating reports—by managing multi-step workflows, unlike traditional chatbots.

An agent is defined as a system that uses an LLM to control workflows, access external tools, and operate within defined boundaries. It can determine when to finish a task, correct errors, or hand control back to the user. The guide recommends using agents in workflows that involve complex decisions, hard-to-maintain rules, or reliance on unstructured data.

An agent’s design is based on three components: a language model for reasoning, tools (APIs or interfaces) for acting and gathering data, and clear instructions. The document explains how to implement them using OpenAI’s Agents SDK and includes practical examples, such as a weather agent. It suggests starting with powerful models to establish a performance baseline, then testing lighter models to reduce cost.

Tools are grouped into three types: data tools, action tools, and orchestration tools. They should be reusable and well documented. Instructions should be based on existing documentation and account for edge cases and common errors. Two orchestration patterns are described: single-agent systems (simpler) and multi-agent systems such as the manager model (one central agent coordinates others) or decentralized model (agents collaborate and transfer control), useful for tasks like support triage.

The guide also highlights the role of guardrails—mechanisms that ensure the agent behaves safely—such as filtering sensitive data, validating relevance, and assessing tool risks. It recommends combining simple rules with LLM-based validation and always enabling human intervention in critical cases.