AgentKit vs Actions: A practical guide to OpenAI's agent builder

Let's be honest, we've all gotten pretty used to AI that can chat. But the conversation is shifting toward something much more interesting: AI that can do things. This is where the real excitement is building, with AI assistants that can actually manage your calendar, process a customer refund, or handle initial support tickets without any hand-holding.

OpenAI recently threw its hat in the ring with AgentKit, a new toolkit aimed at making it easier for anyone to build these action-oriented agents. The promise is a slick, visual way to create AI assistants that can pull their own weight.

But what's the real story behind the buzz? This guide will give you a straight-up, practical look at AgentKit. We'll break down what it is, how it helps AI take "actions," and take an honest look at where it shines and where it falls short, especially for teams trying to automate real-world business tasks like customer support.

Core concepts: A look at AgentKit vs Actions

Before we dive into the tool itself, let’s make sure we’re on the same page with the basic ideas. What in the world is an "action," and how does AgentKit fit in?

What are AI 'actions'?

Simply put, an "action" is any task an AI does that isn't just spitting out text. Think of it as the difference between an AI telling you how to do something and the AI just doing it for you.

Here are a few examples you'd run into in a typical business:

• Looking up a customer's order status in Shopify.

• Updating a contact’s info in your CRM.

• Creating a new support ticket in Zendesk.

• Tagging an incoming email based on its contents.

These actions are what upgrade a chatbot from a simple Q&A bot into a genuinely helpful assistant that can actually lighten your team's workload.

What is AgentKit?

AgentKit is OpenAI’s toolkit for putting together, testing, and launching AI agents that can perform those kinds of actions. It’s not just one thing, but a collection of parts that work together:

• Agent Builder: A visual canvas where you design your agent's logic and workflow.

• ChatKit: A chat interface you can embed so people can talk to your agent.

• Guardrails & Evals: Tools to help you make sure your agent behaves properly and does its job right.

It's worth mentioning that a few other companies use the name "AgentKit" for their own internal tools (like the consulting firm BCG X). In this article, we're talking exclusively about OpenAI's official product.

How the visual builder works

The Agent Builder is the core of AgentKit. It's where you map out your agent’s "brain" and define the logic it uses to get from point A to point B.

The drag-and-drop canvas

If you’ve ever used a flowchart tool or something like Canva, the Agent Builder will feel pretty familiar. It's a visual space where you connect different building blocks, called "nodes," to map out a workflow.

The main node types you'll be playing with are:

• Agent: The main language model that thinks and makes decisions.

• Tool: These are the connectors that let your agent talk to the outside world. This could be searching through documents you've uploaded or connecting to external apps and services.

• Logic: These are nodes like "If/Else" that let you create different paths for your agent to follow based on what's happening.

You wire these nodes together to create a step-by-step process. A user's request comes in, it hits an Agent node to be understood, which might then trigger a Tool node to grab some data, and so on. This visual setup is meant to be more intuitive than having to write all the logic in code.

The downside of rigid workflows

While the visual builder looks great on the surface, its biggest weakness is that everything has to happen in a strict, sequential order. An agent can't cleverly pick between multiple tools on its own; you have to manually map out every single decision path using those "if/else" nodes.

For a simple, one-off task, that’s totally fine. But the minute your logic gets a bit more complicated, things can get messy. Imagine a workflow for checking an order status, then checking inventory, then offering a replacement if the item is out of stock. Your canvas can quickly become a tangled web of branches and connections. This design puts all the pressure on you to think of and map out every possible scenario ahead of time.

Visual builders are handy for simple flows, but trying to manage complex support logic this way can be a real headache. In contrast, platforms like eesel AI let you set up powerful automation with a much simpler rules engine. You can define exactly which tickets should be automated and which should go to a human, without getting lost in a complicated flowchart.

Limitations for enterprise support teams

AgentKit is a really interesting piece of tech, but for teams wanting to put it in front of actual customers, there are a few major gaps that make it a tough choice for real-world use.

Vendor lock-in: A key issue

First off, AgentKit only works with OpenAI's models (like GPT-4). You can't just swap in another model, like Anthropic's Claude if you need its reasoning skills, or a cheaper open-source alternative to save on costs.

This kind of vendor lock-in has real business consequences. You're completely tied to OpenAI's pricing, their performance, and whatever they decide to do next. If a better, cheaper, or more specialized model comes out from someone else, you're out of luck unless you want to rebuild your entire agent from scratch on a new platform. In the fast-changing world of AI, that's a pretty big risk.

Feature	AgentKit	eesel AI
Model Flexibility	OpenAI models only (GPT-4)	Works with any model (OpenAI, Claude, open-source)
Vendor Dependence	High (tied to OpenAI's roadmap and pricing)	Low (platform-agnostic)
Future-Proofing	Low (risk of needing a full rebuild)	High (can adapt to new, better models)

The knowledge gap

An agent is only as smart as the information it has access to. AgentKit mostly learns from files you have to manually upload. It has no built-in, automatic way to sync with knowledge sources that are always changing, like your company’s help center, internal wiki, or project boards.

This creates a massive amount of upkeep. Every time a help article is updated, a policy changes, or a new product spec is added, someone has to remember to go into AgentKit and upload the new document. If they forget, your agent starts giving out wrong information. It's a recipe for failure.

Even more importantly, AgentKit gives answers without telling you where it got the information from. For customer support, that’s a deal-breaker. Without citations, neither a customer nor a human agent can double-check the information. This kills trust and makes it unusable for any situation where accuracy is a top priority.

This is where a purpose-built tool really stands apart. eesel AI was designed to solve this exact problem. It unifies your company knowledge by instantly and continuously syncing with all your sources, from help desks like Zendesk and Intercom to internal wikis like Confluence. It even learns from your team's past ticket resolutions, making sure its knowledge is always fresh and relevant.

Security and rollout concerns

Making it easier for anyone to build agents is great, but it also opens the door to new risks, like having dozens of unmanaged agents floating around or creating new security vulnerabilities. Before you let an AI loose on your customers and company data, you need to be 100% sure it’s going to behave itself.

Without a solid way to test and simulate how your agent will perform, you're basically guessing. One bad interaction or one wrong action can seriously damage customer trust and your company’s reputation.

This is why a powerful simulation mode is a must-have for serious teams. Before you turn anything on, eesel AI lets you test your AI agent on thousands of your past tickets. This gives you a clear picture of how it will perform and what its resolution rate will be, so you can spot any issues and launch with total confidence.

Pricing and developer experience

Beyond the functional limits, the way you pay for it and the workflow for developers are two more things to think about before jumping on a new platform.

How pricing works

AgentKit doesn't have its own separate price tag. Instead, its cost is tied directly to how much you use the OpenAI API. You get billed for the input and output tokens your agent uses, plus any other fees for things like file storage.

This pay-as-you-go model can make it really hard to predict your monthly costs. A sudden jump in customer questions could lead to a shockingly high bill, making it tough to budget for your support team.

A screenshot showing the pay-as-you-go pricing model for OpenAI's AgentKit, which can make costs unpredictable.

For businesses that need their budgets to be predictable, this can be a real issue. For comparison, eesel AI offers clear, predictable pricing plans with no surprise fees per resolution, so you know exactly what you’re paying each month.

The one-way street from visual to code

AgentKit offers SDKs for Python and TypeScript, which sounds great for developers who want more fine-grained control. The problem is, the connection between the visual builder and the code only goes one way. You can export a workflow you built on the canvas to code, but you can't bring any code changes back into the visual editor.

Even worse, this export feature gets completely shut off the moment you add a connection to an external tool, which is the main way to let your agent take any real action.

This creates a major disconnect between the non-technical people using the visual builder and the developers working in code. A product manager can't mock something up and hand it off to an engineer to finish, and an engineer can't show a complex workflow to stakeholders in a simple, visual way. This fractured experience makes it nearly impossible for teams to collaborate effectively.

The verdict: Is AgentKit right for you?

When you lay it all out, it's clear that AgentKit is an ambitious tool, but it comes with some serious strings attached.

AgentKit could be a good fit for:

• Developers and teams who are already all-in on the OpenAI ecosystem.

• Projects where having a polished, ready-to-embed chat interface is the most important thing.

• Building simple, straightforward agent workflows that don't need complicated logic or the flexibility of using different AI models.

AgentKit is probably not a good fit for:

• Enterprise support teams that need to pull information from many different, constantly updated sources.

• Any situation where you need audit trails and source citations for compliance or trust.

• Teams that need predictable pricing and a setup they can manage themselves without a ton of technical help.

A better way: Build enterprise-ready AI agents in minutes

AgentKit’s visual builder is a step in the right direction for making AI agents more accessible, but its rigid structure, reliance on manual knowledge updates, and lack of enterprise-level features make it a tough sell for real-world business use. It gives you the parts but leaves you to figure out the hardest problems of integration, maintenance, and safety.

If you're looking to launch an autonomous AI agent that connects smoothly with your existing tools, learns from your company's own knowledge, and starts delivering value right away, try eesel AI for free. You can go live in minutes with a platform that gives you total control, unified and self-updating knowledge, and risk-free simulation, all with pricing that makes sense.