Is OpenAI's AgentKit the best agentic workflow builder available?

Over the past year, large language models (LLMs) have evolved from simple chat systems into programmable reasoning engines. Developers have been using them to plan, retrieve data, and take structured actions. However, one challenge has remained consistent: building these agents reliably and safely required too much custom infrastructure.

Even advanced teams had to handle multiple layers manually, prompt logic, tool integration, UI design, evaluation pipelines, and deployment. This slowed down experimentation and made it difficult to manage versioning, safety, and monitoring at scale.

OpenAI’s AgentKit, introduced in October 2025, addresses that gap. It provides a unified platform for designing, evaluating, and deploying agents on top of OpenAI’s models, including GPT-4o.
The goal is to make agentic systems easier to build, understand, and maintain, whether they power a customer-support assistant, automate engineering workflows, or integrate with enterprise tools.

What do we mean by agents?#

An agent is a system that can reason about a goal, choose tools or APIs to use, and execute actions to achieve that goal.

In contrast to a single prompt-response interaction, an agent:

• Maintains state across steps.

• Uses tools and connectors (for example, APIs or file systems).

• Follows logic or workflows that guide how it behaves.

These workflows can include conditionals, loops, guardrails, and integrations with real data sources.

OpenAI designed AgentKit to make each of these components built-in features rather than ad-hoc code. The result is a consistent environment for agent development, similar to what the OpenAI Playground did for prompts, but at the level of full systems.

The purpose of AgentKit#

AgentKit provides the essential components for the entire agent life cycle:

It allows teams to:

• Visually design workflows.

• Connect to internal and external systems through secure connectors.

• Test agent decisions step-by-step.

• Apply safety guardrails.

• Embed agents directly into applications through standardized interfaces.

This design supports both individual developers experimenting with prototypes and organizations building production-grade automation.

What AgentKit includes and how it works#

AgentKit is a composable platform for building agents on top of OpenAI models. Each part is designed to handle one layer of the agent life cycle, from construction and testing to deployment and monitoring. The platform’s architecture reflects a principle OpenAI calls “agentic workflows,” where an agent is classified as a predictable system that can reason, act, and be evaluated within clear boundaries.

Below is an overview of the key components.

1. Agent builder#

The agent builder is the central workspace of AgentKit. It lets you visually design how an agent operates, using nodes to represent steps such as model reasoning, tool calls, conditional logic, and user interactions.

Each workflow is expressed as a directed graph.

• Input nodes define what data the agent receives (for example, a user query or scheduled trigger).

• Model nodes handle reasoning, summarization, or decision-making.

• Tool nodes invoke APIs or connectors.

• Branch nodes implement conditional flow (e.g., if X → do Y).

• Guardrail nodes enforce safety and policy constraints.

All executions are traced, meaning every model output and tool call is logged for inspection. This traceability is critical for debugging and evaluation.

The agent builder supports both no-code composition and programmatic control through the OpenAI Agents SDK. Developers can design a flow visually and then export or embed it in code for production.