OpenAI Agent Builder training, 1-on-1.You design and ship your AI agents.
An OpenAI Agent Builder expert opens your use case with you and builds what matters: a visual agent workflow on the canvas, tools and guardrails, the connection to your data and MCP, then deployment via ChatKit or the API. We start from your real case, not theory.
★★★★★ 4.7/5 · 300+ pros trained · France Num certified
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GeminiWe ship OpenAI agents for real clients, not just in theory.
Most Agent Builder trainings are recorded tutorials by people who opened the tool the night before. At Hack'celeration it's the opposite: designing workflows on the canvas, wiring up tools and guardrails, connecting MCP servers, deploying via ChatKit and the API, that's our daily agency work. Everything we teach you, we practice on agents in production. We know the traps (the agent with no guardrail that goes off the rails, the badly scoped MCP, the loop that blows up the token bill) because we've already solved them.
- We ship OpenAI agents for real clients every week, not just in theory
- 1-on-1 format: the trainer adapts to your level, from no-code to seasoned dev
- We tell you when Agent Builder isn't the right call (sometimes plain code or a simple prompt is enough)
- We start from your real use case and your stack, not a dummy example
Four pillars so your agent goes from canvas to production.
A badly handled Agent Builder means a workflow that only talks, with no tools or guardrails, never deployed. Most of the trouble comes from design and integration, not the tool. We pick up your real use case and work through the four pillars together.
- Visual workflow
Design your first agent on the canvas, not all in code
Agent Builder is OpenAI's visual editor (shipped inside AgentKit in late 2025): you drop nodes on a canvas and connect the steps instead of hand-writing all the orchestration. We start from one of your real cases (ticket triage, lead qualification, internal assistant) and build an agent workflow that runs end to end, not a throwaway demo.
Design my agent - Tools, guardrails and logic
Give it tools, and give it guardrails
An agent that only talks is useless. We wire it up with tools (function calling, search, code), add routing logic and conditions between nodes, and above all set guardrails: filter off-topic input, block sensitive data (PII), validate outputs. You know exactly what your agent is allowed to do, and what it will never do.
See tools and guardrails - Connect your data
Plug it into your stack, your files, your APIs, MCP
A useful agent has access to your real data. We connect your files and knowledge bases (file search, retrieval), your internal APIs, and your MCP servers (Model Context Protocol) so it can read and act inside your tools: CRM, database, docs. We wire it cleanly, with the right scopes, so it answers on your real context instead of generic fluff.
See the data connection - Test, deploy, integrate
Test, deploy and integrate into your product
Building the agent isn't enough: it has to live in production. We test it (evals, edge cases, hallucinations), we deploy it, then we integrate it via ChatKit to drop it as a chat inside your product, or via the API into your back-end and your Make and n8n automations. We version it to iterate without breaking, and you leave with an agent wired where your users are.
Plan the deployment
Meet our trainers, leave with a plan.
Drop your email. We get back to you to connect you with a Hack'celeration-certified trainer: we look at your use case, spot the agent Agent Builder can get you building, and tell you where to start. No commitment, even if you don't take the training.
- A diagnosis of your use case and the agent to build
- The first workflow steps, in priority order
- The right 1-on-1 format for your level and your stack
- An honest take: Agent Builder or code for your case
Your OpenAI Agent Builder program, step by step.
Five steps, no skipping. Each one on your real use case, with a clear deliverable. From the first session we scope the agent to build. By the end, you build and evolve your agents without us.
- Step 1 · Use-case audit
We scope the agent to build and what it must do
First session, we look at what you actually need: ticket triage, lead qualification, an internal assistant over your docs, automating a repetitive task. We define what the agent must do, what data it should access, and what it must never do. We also check whether Agent Builder is the right tool for this case, or whether a simple prompt or plain code would do better. You leave with a clear scope and a list of steps, in priority order. No theory, your real case.
- Step 2 · First visual workflow
We build your agent workflow on the canvas
We open Agent Builder and build your first workflow together. We drop the nodes, connect the steps, configure the model and the agent instructions. You learn to think in workflows (input, steps, output) rather than one giant prompt, and to read what each node does. By the end of this step, you have an agent running on your case, simple but real, and you can add or reorder steps yourself.
- Step 3 · Tools, guardrails and data
We wire up tools, guardrails and your data
This is where the agent gets useful. We add the tools (function calling, search, code), the routing logic between nodes, and the guardrails to filter off-topic input, block PII and validate outputs. Then we connect it to your real data: file search over your documents, your internal APIs, and your MCP servers so it can act inside your CRM or database. You practice on your own stack, not a sandbox. You finish with an agent that answers on your real context, inside a frame you control.
- Step 4 · Deployment and integration
We test, deploy, and integrate it into your product
We push the agent to production. We test it on edge cases and hunt for hallucinations with evals, we deploy it, then we integrate it where your users are: ChatKit to drop it as a chat inside your product, or the API into your back-end and your Make and n8n automations. We version it to iterate without breaking what works. This is the step where most people stall on their own. We run it until your agent is really wired in.
- Step 5 · Autonomy
You build and evolve your agents without us
The number one goal: you become autonomous. By the end of the program, you know how to design a workflow in Agent Builder, add tools and guardrails, connect it to your data and MCP, test it and deploy it via ChatKit or the API. You no longer need an agency to ship an AI agent. And if you want to delegate a bigger build later, we also run an OpenAI Agent Builder agency, but that's not the point here.
Why train 1-on-1 with us.
- 300+Pros already trained on AI
More than 300 people have gone through our trainings across France and Europe. Devs, founders, ops and support teams. Not vanity numbers: people who designed a real agent in Agent Builder and shipped it, instead of getting stuck on a YouTube tutorial.
- 4.7/5Rating across 334 verified reviews
Average rating of 4.7 out of 5, across 334 reviews. We won't pretend Agent Builder handles everything: a complex agent needs testing and follow-up, and sometimes code on the side. But the 1-on-1 format makes the difference in going from the canvas to an agent that holds up in production.
- 1:1A dedicated expert, not a class of 100
You're not a number in a webinar. A trainer opens Agent Builder on your real use case, looks at your stack, and works through your actual workflow. We schedule sessions around your availability, replays included.
A working agency, recognized by the French State.
Hack'celeration is certified Activateur France Numérique and holds the AI Ambassador title, both granted by France Num to organizations that genuinely drive the digital transformation of companies. On the ground, we ship OpenAI agents for clients every week: more than 300 pros trained and a 4.7/5 rating across 334 verified reviews, left by the people who took our programs, not just by the buyer.
- Certified Activateur France Numérique
- AI Ambassador (France Num)
- 300+ pros trained across France and Europe
- 4.7/5 across 334 verified reviews
The questions we get the most.
What is a 1-on-1 OpenAI Agent Builder training?
An individual program with an expert, not a class of 100 people. We open Agent Builder on your real use case, look at your stack, and work through your actual workflow: design the agent visually, add tools and guardrails, connect your data and MCP, test and deploy via ChatKit or the API. You ask your questions live, the trainer adapts the pace to your level. We schedule sessions around your availability, and you leave with concrete actions every time. That's the difference between watching a tutorial and actually shipping an agent.How much does the OpenAI Agent Builder training cost?
There is no single price. We connect you with a trainer certified by Hack'celeration, matched to your need and your level. It varies from one trainer to another, based on their profile and the format that fits your project.What is OpenAI Agent Builder and AgentKit?
Agent Builder is OpenAI's visual editor for designing agents and multi-step workflows: you drop nodes on a canvas, connect the steps, add tools and guardrails, instead of hand-writing all the orchestration. It's part of AgentKit, OpenAI's suite for building agents, which also includes the Connector Registry, ChatKit for the chat interface, and evaluation tools. In training, we focus on Agent Builder and touch the rest of AgentKit based on your deployment needs.Agent Builder or coding with LangChain: which one to pick?
It depends on your team and your case. Agent Builder is visual: you see your workflow, you iterate fast, and a less technical profile can follow, which is ideal for prototyping and standard agents. A code framework like LangChain gives finer control, code-based versioning and custom logic, at the cost of more engineering. The right move isn't one versus the other: we help you start visually in Agent Builder, and move to the code side (or mix with the API) when your agent truly warrants it. We're honest about the limit.Do I need to code to use Agent Builder?
Not to start. The canvas is visual: you build your first workflow, add nodes and guardrails without writing code. For custom tools (function calling on your own APIs) or an API integration, a bit of technical comfort helps, but the 1-on-1 format starts from your exact level: beginner, we go step by step on the visual side; seasoned dev, we jump straight to custom tools, MCP and API deployment. You learn exactly the layer you need.What is a guardrail, and why does it matter?
A guardrail is a protection you put around your agent to control what it does. Concretely: filter off-topic messages, block or mask sensitive data (PII), detect a jailbreak attempt, and validate that the output matches the expected format before returning it. Without guardrails, an agent in production can answer anything or leak info. In training, we configure guardrails on your real case so you know exactly what your agent is allowed to do, and what it will refuse.Can I connect Agent Builder to my data and tools via MCP?
Yes, and it's often what makes the agent genuinely useful. You can give it access to your files and knowledge bases (file search, retrieval), your internal APIs, and your MCP servers (Model Context Protocol) so it can read and act inside your tools: CRM, database, docs. AgentKit's Connector Registry helps manage these connections. In 1-on-1, we wire this on your real stack, with the right scopes and permissions, so the agent answers on your context instead of generic output.How do I deploy and integrate an Agent Builder agent into my product?
Once the agent is ready and tested, you have two main paths. ChatKit lets you drop a clean chat interface straight into your product or site, with your agent behind it. The API lets you call it from your back-end, your scripts, or no-code tools like Make and n8n to wire it into your existing automations. We version the workflow to iterate without breaking production. In training, we run the path that fits your product, until the agent is truly live.How much does running an agent built with Agent Builder cost?
Agent Builder is the editor; what you pay behind it is mostly the OpenAI model usage (the tokens consumed by each call) and the tools your agent uses. A multi-step workflow with search and several nodes consumes more than a single call, so the cost depends on traffic and complexity. In training, we set up cost guardrails (pick the right model per step, cap the context, avoid useless loops) so your agent stays profitable once in production, not just a demo.Is the training online or in person?
100% online, over video, 1-on-1. You join the sessions from anywhere, we share your screen and Agent Builder on your real case live. Sessions are recorded if you want to revisit them. The individual format means real interaction: you're not a number in a webinar of 100, the trainer answers your questions about your workflow and your stack. That's what makes learning concrete on a tool as new as Agent Builder.
Your AI agent deserves to exist. Meet your trainer.
Drop your email. An expert who ships OpenAI agents daily looks at your use case and shows you how to go from the canvas to an agent in production. No commitment, even if you don't take the training.