Build AI Agents Easily With OpenAIs AgentKit

A New Toolkit for Action-Taking AI

AgentKit is a powerful toolkit from OpenAI that enables developers to build and embed ChatGPT-powered agents directly into their websites and applications. First announced at OpenAI’s 2025 DevDay, AgentKit is designed to create AI agents that go beyond simple conversation to perform actions like browsing the web, calling APIs, and executing complex, multi-step tasks.

In short, AgentKit supplies the essential architecture and components needed to integrate a ChatGPT-like assistant seamlessly into your product. This post explores AgentKit's architecture, its core building blocks, and how it empowers developers to add sophisticated AI functionality to any website.

The Core Architecture of AgentKit

AgentKit is built upon two fundamental backend components that power its entire feature set: the Responses API and the Agents SDK.

Responses API: This component manages structured outputs and the use of external tools through OpenAI's function-calling interface. When an agent needs to use a tool, the Responses API formats the request, executes the call, and returns the data in a structured way that the agent can easily process. This eliminates the developer's need to manually parse outputs or handle integration errors.

Agents SDK: This SDK serves as the agent's runtime and orchestration layer. It is responsible for managing the conversation's state, sequencing tasks that require multiple tool calls, handling errors and retries, and enforcing the agent's logic. It allows the agent to maintain context and reasoning across several steps, removing the need for developers to write boilerplate code for prompt chaining or context tracking.

In practice, the Responses API plus the Agents SDK means developers don’t have to reinvent low-level infrastructure for conversation management or tooling.

This two-layer architecture provides out-of-the-box capabilities for conversation and tool management. Workflows can be defined visually or with code, while the platform handles response streaming, state management, and tool orchestration automatically. This approach accelerates development and enhances reliability with built-in error handling and safety measures.

Safety is a central part of AgentKit’s design. The platform incorporates input validation, output filtering, and PII masking to guard against malicious prompts and data leaks. These security guardrails scan information entering and leaving the model, reducing the risks associated with deploying AI agents that handle user data. Developers can customize the strictness of these guardrails, ensuring that embedded agents behave responsibly.

The Building Blocks for Powerful AI Agents

AgentKit offers a collection of modular components designed to help you build, deploy, and embed ChatGPT-powered agents efficiently. These tools abstract away common functionalities, letting you concentrate on your specific use case.

Agent Builder: This is a visual workflow editor for designing an agent's conversational logic. Developers can drag and drop nodes representing prompts, tool calls, and conditional branches to define the agent's behavior. The Agent Builder also supports versioning and previews, allowing for safe iteration on agent designs without impacting the production environment. This visual method speeds up prototyping for complex agents and fosters collaboration between technical and non-technical team members.

Connector Registry: This is a library of pre-built integrations for connecting agents to external systems like databases, SaaS applications, and internal APIs. The registry includes out-of-the-box connectors for services such as Dropbox, Google Drive, and Microsoft Teams. Each connector manages authentication, API calls, and errors, allowing you to plug them into your workflow without writing custom integration code. Developers can also create and share custom connectors for systems not yet available in the registry.

ChatKit: An embeddable UI toolkit for deploying the agent's chat interface on your website or application. ChatKit provides a complete front-end experience, including message display, streaming responses, and conversation history, saving you from building a chat UI from scratch. It is highly customizable to match your site's branding and uses WebSockets for a smooth, interactive experience. ChatKit makes it incredibly easy to embed a ChatGPT-style assistant on any site.

Evaluation and Tracing Tools: AgentKit includes tools for evaluating and debugging agent performance. You can create test datasets to measure accuracy and response quality. The platform also provides detailed trace logs for each conversation, showing the agent's step-by-step reasoning. These features are crucial for understanding an agent's decisions and systematically improving its performance in a production environment.

AgentKit supports continuously improving agents through feedback.

Reinforcement Learning and Improvement Loops: The toolkit supports continuous agent improvement through feedback. Developers can define reward functions and success metrics to fine-tune an agent’s behavior over time. This allows an embedded agent to become more effective and tailored to your specific domain as it interacts with users.

Built-in Guardrails: As previously noted, safety is a key component. AgentKit integrates OpenAI Guardrails to provide configurable policies for content filtering and safe actions. You can enable guards to check user inputs, sanitize agent outputs, mask personal data, restrict certain tool usage, and even require human approval for high-stakes actions. These customizable features help ensure that your embedded agent operates within safe and acceptable limits.

How The Components Work Together

When you use AgentKit to embed ChatGPT into a website, these components function in concert. You use the Agent Builder to define the agent's logic, the Connector Registry to link it to necessary data and services, and ChatKit to provide the user interface. Meanwhile, the evaluation and monitoring tools help you track and refine the agent's performance. The underlying Responses API and Agent SDK handle all the heavy lifting, like managing dialogue and interacting with the GPT models, allowing you to focus on creating value.

Conclusion: The Future of Embedded AI

OpenAI’s AgentKit marks a major advancement in making sophisticated AI accessible within everyday software. Its well-designed architecture abstracts the complexities of AI development, allowing developers to easily integrate intelligent agents into their products.

The core components provide a comprehensive toolkit for designing, deploying, and managing agents that can automate customer support, enhance productivity tools, and streamline business processes. For developers, AgentKit offers a faster and more reliable way to bring an AI assistant to life on any website or application. With built-in tools for continuous improvement, the vision of having a helpful, action-taking AI on every website is quickly becoming a reality.