How to Build an AI Agent with OpenAI: A Practical Guide

OpenAI has released a comprehensive guide titled A Practical Guide to Building Agents, aimed at assisting product and engineering teams in developing AI agents powered by large language models (LLMs). This guide consolidates insights from various customer deployments into actionable best practices for building effective and safe AI agents.

What Is an AI Agent?

An AI agent is a system that autonomously performs multi-step tasks on behalf of users, leveraging LLMs to manage workflow execution and make decisions. Unlike traditional software that automates specific tasks, agents can handle entire workflows with a high degree of independence. They can recognize when a workflow is complete, proactively correct actions if needed, and halt execution to transfer control back to the user in case of failure.

When Should You Build an Agent?

Agents are particularly suited for workflows where traditional deterministic and rule-based approaches fall short. Consider building an agent when:

• Complex Decision-Making: Workflows involve nuanced judgment or context-sensitive decisions, such as refund approvals in customer service.

• Difficult-to-Maintain Rules: Systems have become unwieldy due to extensive and intricate rulesets, making updates costly or error-prone, like performing vendor security reviews.

• Heavy Reliance on Unstructured Data: Scenarios involve interpreting natural language, extracting meaning from documents, or interacting with users conversationally, such as processing insurance claims.

Core Components of an AI Agent

Building an effective AI agent involves three foundational components:

1. Model: The LLM that powers the agent's reasoning and decision-making.

2. Tools: External functions or APIs the agent can use to gather context or take actions.

3. Instructions: Explicit guidelines and guardrails that define how the agent behaves, including task breakdowns and handling of edge cases.

Orchestration Patterns

Depending on the complexity of tasks, agents can be orchestrated in different patterns:

• Single-Agent Systems: One agent handles tasks with incrementally added tools.

• Multi-Agent Systems: Workflows are distributed across multiple agents, either managed by a central agent (Manager Pattern) or decentralized with agents handing off tasks to each other.

Implementing Guardrails

Given their autonomy, agents require robust safety measures to manage risks and ensure alignment with user expectations. Guardrails include:

• Input Checks: Ensuring relevance and safety of inputs, such as blocking irrelevant or potentially harmful prompts.

• Output Checks: Maintaining appropriate tone and avoiding offensive content in responses.

• Tool Safeguards: Restricting access to sensitive tools or requiring approvals for high-stakes actions.

• Human Intervention: Escalating tasks to humans in cases of failure, edge cases, or high-risk actions, especially during initial deployment.

Getting Started

To begin building your AI agent:

1. Identify Suitable Workflows: Focus on processes that involve complex decisions, unstructured data, or brittle rule-based systems.

2. Assemble Core Components: Choose an appropriate LLM, integrate necessary tools, and define clear instructions.

3. Implement Guardrails: Establish safety measures to manage risks and ensure predictable behavior.

4. Start Small and Iterate: Begin with a single-agent system, validate with real users, and gradually expand capabilities as needed.

Code Example: Building a Simple AI Agent

Here's a simplified example of how to build an AI agent using OpenAI's API:

import { OpenAI } from 'openai';

// Initialize the OpenAI client
const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

// Define tools the agent can use
const tools = [
  {
    type: "function",
    function: {
      name: "get_weather",
      description: "Get the current weather in a given location",
      parameters: {
        type: "object",
        properties: {
          location: {
            type: "string",
            description: "The city and state, e.g., San Francisco, CA",
          },
          unit: {
            type: "string",
            enum: ["celsius", "fahrenheit"],
            description: "The unit of temperature",
          },
        },
        required: ["location"],
      },
    },
  },
];

// Function to implement the weather tool
async function getWeather(location, unit = "celsius") {
  // In a real implementation, this would call a weather API
  console.log(`Getting weather for ${location} in ${unit}`);
  return { temperature: 22, unit: unit, condition: "Sunny" };
}

// Main agent function
async function runAgent(userInput) {
  // Step 1: Start a conversation with the model
  const messages = [
    { role: "system", content: "You are a helpful assistant that can check weather information." },
    { role: "user", content: userInput }
  ];
  
  try {
    // Step 2: Get the model's response
    const response = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: messages,
      tools: tools,
      tool_choice: "auto",
    });
    
    const responseMessage = response.choices[0].message;
    
    // Step 3: Check if the model wants to use a tool
    if (responseMessage.tool_calls) {
      // Add the model's response to messages
      messages.push(responseMessage);
      
      // Process each tool call
      for (const toolCall of responseMessage.tool_calls) {
        if (toolCall.function.name === "get_weather") {
          // Parse the arguments
          const args = JSON.parse(toolCall.function.arguments);
          
          // Call the function
          const functionResponse = await getWeather(args.location, args.unit);
          
          // Add the function response to messages
          messages.push({
            role: "tool",
            tool_call_id: toolCall.id,
            content: JSON.stringify(functionResponse),
          });
        }
      }
      
      // Step 4: Get the final response from the model
      const secondResponse = await openai.chat.completions.create({
        model: "gpt-4o",
        messages: messages,
      });
      
      return secondResponse.choices[0].message.content;
    } else {
      // If no tool was called, return the direct response
      return responseMessage.content;
    }
  } catch (error) {
    console.error("Error:", error);
    return "I encountered an error while processing your request.";
  }
}

// Example usage
runAgent("What's the weather like in San Francisco?").then(response => {
  console.log("Agent response:", response);
});

This example demonstrates a simple AI agent that can check the weather for a given location. In a real-world application, you would expand this with more tools, better error handling, and guardrails as discussed in the guide.

Conclusion

For a detailed walkthrough and more comprehensive code examples, refer to OpenAI's full guide:

👉 Download the PDF

This guide serves as a valuable resource for teams looking to harness the power of AI agents to automate complex workflows effectively and safely. As AI agent technology continues to evolve, following these best practices will help ensure your implementations are robust, safe, and deliver real value to users.