A Practical Guide to Building AI Agents with OpenAI

While conventional software automates workflows, agents perform these workflows independently on behalf of users with a high degree of autonomy. An agent is essentially a system designed to accomplish tasks for you without constant supervision. A workflow represents a sequence of steps required to achieve a user's goal, whether it's resolving a customer service issue, booking a reservation, committing code, or generating a report. Applications that integrate LLMs but do not use them to control workflow execution—such as simple chatbots, single-turn LLMs, or sentiment classifiers—are not considered agents. Key characteristics that enable agents to act reliably and consistently on behalf of a user include: * **LLM-driven Decision-Making:** Agents leverage an LLM to manage workflow execution and make critical decisions. They can recognize when a workflow is complete and proactively correct their actions if necessary. In the event of a failure, they are designed to halt execution and transfer control back to the user. * **Tool Integration and Dynamic Selection:** Agents have access to a variety of tools that allow them to interact with external systems, both for gathering context and for taking actions. They dynamically select the appropriate tools based on the workflow's current state, always operating within clearly defined guardrails.

At its core, an agent is comprised of three fundamental components: 1. **Model:** This is the Large Language Model (LLM) that powers the agent's reasoning and decision-making capabilities. 2. **Tools:** These are external functions or APIs that the agent can utilize to perform actions and interact with the outside world. For legacy systems lacking APIs, agents can employ computer-use models to interact directly with applications and systems through web and application UIs, mimicking human interaction. 3. **Instructions:** These are explicit guidelines and guardrails that define the agent's behavior, operational parameters, and decision-making processes. These concepts can be implemented using frameworks like OpenAI's Agents SDK, or built from scratch using your preferred libraries. For instance, a simple weather agent might be defined with its name, instructions, and a tool to fetch weather data: ```python from agents import Agent weather_agent = Agent( name="Weather agent", instructions="You are a helpful agent who can talk to users about the weather", tools=[get_weather], ) ``` Each tool should possess a standardized definition to facilitate flexible, many-to-many relationships between tools and agents. Well-documented, thoroughly tested, and reusable tools enhance discoverability, simplify version management, and prevent redundant definitions.

Tools are the extensions that empower your agent's capabilities by enabling interaction with underlying applications and systems through APIs. For older systems lacking APIs, agents can leverage computer-vision models to interact directly with application UIs, much like a human would. Each tool must have a standardized definition to allow for flexible, many-to-many relationships between tools and agents. Well-documented, thoroughly tested, and reusable tools significantly improve discoverability, simplify version management, and prevent the creation of redundant definitions. Broadly, agents require three types of tools: * **Data Tools:** These tools enable agents to retrieve context and information essential for executing workflows. Examples include querying transaction databases or CRMs, reading PDF documents, or performing web searches. * **Action Tools:** These tools allow agents to interact with systems to perform actions, such as adding new information to databases, updating records, or sending messages. Examples include sending emails and texts, updating CRM records, or handing off a customer service ticket to a human agent. * **Orchestration Tools:** Agents themselves can function as tools for other agents, as seen in the Manager Pattern within the Orchestration section. Examples include a Refund Agent, a Research Agent, or a Writing Agent. Here's an example of equipping an agent with tools using the Agents SDK: ```python from agents import Agent, WebSearchTool, function_tool import datetime @function_tool def save_results(output): db.insert({ "output": output, "timestamp": datetime.datetime.now(), }) return "File saved" search_agent = Agent( name="Search agent", instructions="Help the user search the internet and save results if asked.", tools=[WebSearchTool(), save_results], ) ``` As the number of required tools grows, consider distributing tasks across multiple agents, as detailed in the Orchestration section.

With the foundational components in place, the next step is to consider orchestration patterns that enable your agent to execute workflows effectively. While the temptation might be to immediately build a fully autonomous agent with a complex architecture, customer experience often shows that an incremental approach yields greater success. Generally, orchestration patterns fall into two main categories: 1. **Single-Agent Systems:** In this model, a single LLM, equipped with appropriate tools and instructions, executes workflows in a loop until an exit condition is met. 2. **Multi-Agent Systems:** Here, workflow execution is distributed across multiple coordinated agents, allowing for specialized roles and parallel processing. Let's delve into each pattern in detail.

While it's often recommended to maximize a single agent's capabilities first, complex workflows may benefit from splitting prompts and tools across multiple agents, leading to improved performance and scalability. This becomes particularly relevant when agents struggle to follow intricate instructions or consistently select incorrect tools. In such cases, further dividing the system and introducing more distinct agents can be advantageous. Practical guidelines for splitting agents include: * **Complex Logic:** When prompts involve numerous conditional statements (multiple if-then-else branches) and prompt templates become difficult to scale, consider distributing each logical segment across separate agents. * **Tool Overload:** The challenge isn't solely the number of tools but their similarity or overlap. Some implementations successfully manage over 15 well-defined, distinct tools, while others struggle with fewer than 10 overlapping tools. Employ multiple agents if improving tool clarity—through descriptive names, clear parameters, and detailed descriptions—does not enhance performance. Multi-agent systems can be designed in various ways, but customer experiences highlight two broadly applicable categories: 1. **Manager (Agents as Tools):** A central "manager" agent orchestrates specialized agents via tool calls, with each specialized agent handling a specific task or domain. This pattern is ideal when a single agent needs to control workflow execution and maintain direct access to the user. 2. **Decentralized (Agents Handoff to Agents):** Multiple agents operate as peers, handing off tasks to one another based on their specializations. This pattern is optimal when a single agent doesn't need to maintain central control or synthesis, allowing each specialized agent to take over execution and interact with the user as needed. Regardless of the orchestration pattern, the core principles remain: keep components flexible, composable, and driven by clear, well-structured prompts.

Building effective AI agents involves a strategic combination of selecting the right LLM models, defining robust tools, crafting clear instructions, and implementing appropriate orchestration patterns. The journey often begins with maximizing the capabilities of a single agent, incrementally adding tools and refining instructions to handle increasingly complex workflows. When complexity outgrows single-agent capabilities, multi-agent systems, such as the Manager or Decentralized patterns, offer scalable solutions. Throughout this process, maintaining flexibility, composability, and a focus on clear, well-structured prompts is paramount. By adhering to these principles and best practices, development teams can build AI agents that operate safely, predictably, and effectively, unlocking new levels of automation and efficiency for a wide range of applications.