How to Build a ChatGPT Agent

If you've seen the ChatGPT Agents demos floating around — where someone types a single sentence and a fully functional agent appears — we want to save you some time: that's not how it works in practice. Or at least, not if you want it to work well. The one-sentence build is a novelty. What you actually want is a real setup, built from scratch, with the right pieces in the right places. That's what we're walking through here.

What Is a ChatGPT Agent, Really?

First, a quick reality check on terminology. ChatGPT Agents are not necessarily "true blue agentic AI" in the technical sense — they're integrated chatbots. That means they can connect to your apps, follow instructions, use reusable skills, and run on a schedule. That's genuinely useful, even if the word "agent" is doing some heavy lifting.

You'll find the Agents feature in the left-side menu of your ChatGPT window. From there, you can browse existing agent templates or create a new one from scratch. The template library is decent and growing — OpenAI has been expanding agent capabilities steadily throughout 2025 — but for any real business use, you'll want to build your own.

Agents are available on any ChatGPT business subscription. Pricing may shift as the feature matures, so if you're even slightly curious about using this at your company, start experimenting now before the cost calculus changes.

Start Blank. Always.

Here's the most important piece of advice in this whole article: when you go to create a new agent, choose Start Blank in the top right corner. Do not use the chat-based builder as your primary build method.

The builder interface (where you describe what you want and it generates an agent) can be useful for small tweaks and assists — think of it like a drafting tool. But if you rely on it to do the whole job, you'll end up with something buggy, imprecise, and not particularly effective. Start Blank gives you direct control over every component. That control is where the value is.

From the blank canvas, here's what you're working with:

Instructions — This is the operating protocol for your agent. Think of it the way a manager would think about an employee onboarding doc: what does this agent do, how does it behave, what are its guardrails? Be specific. The more precise your instructions, the more predictably it performs.

Project Knowledge — This is where you upload the information your agent needs to do its job. Business processes, brand guidelines, product details, FAQs, example outputs — anything that functions as reference material goes here. Think of it as the agent's brain. If you're building for a specific business context, this section is non-negotiable.

Memory — The agent can retain information over time, which means it gets better with use. It'll remember what it's done, what worked, and relevant context from previous interactions. This is a meaningful feature for anything that involves ongoing or repetitive work.

Skills: The Feature Worth Building a System Around

The Add Skills function is where ChatGPT Agents start to pull ahead of simpler tools. Skills are reusable tasks you program into the agent — specific behaviors it can execute on demand or as part of a workflow.

The smart approach here is to use a separate ChatGPT window to write your skills before plugging them in. Open a new chat, tell it you're building a ChatGPT agent, and ask it to write a skill for a specific behavior — content creation, communication formatting, data summarization, whatever you need. ChatGPT will produce it in the right format, ready to paste in. You can also upload skills if you're doing something more complex.

What makes this genuinely worth building: skills can be saved and shared across multiple agents. So if you create a strong communication skill or a content formatting skill, you don't rebuild it every time. You build a skill library, then pick from it when setting up new agents. For teams running more than one workflow through ChatGPT, this is a real time-saver — and a way to maintain consistency across automations without duplicating effort.

This is one area where it makes sense to think ahead. Build skills with reuse in mind from the start.

Integrations: Connecting Your Agent to the Rest of Your Stack

The Browse Apps section is where you connect your agent to your existing tools. HubSpot, Google Drive, email — if your business data lives somewhere, you can (in many cases) pull it into the agent's context. This is what makes agents useful for actual business work rather than just Q&A.

A few notes from hands-on experience: Microsoft integrations are not yet fully built out, so if your team runs on Teams or SharePoint, manage your expectations for now. Slack, on the other hand, works — you can create a Slack handle for your agent and let your team query it directly within the Slack environment. For teams that live in Slack, that's a significant convenience.

For more advanced integrations, you can connect via custom MCP (Model Context Protocol). If you have some development resources available, MCP allows you to build integrations to nearly any system you have permission to access. This is where the integration potential gets genuinely broad.

Scheduling: Set It and Actually Forget It

One underrated feature: agents can run on a schedule. If there's a function you want executed daily, weekly, or at a specific time — a report pull, a digest summary, a monitoring check — you can set it up to fire automatically without any manual trigger.

This is the feature most people skip in initial setup, and it's one of the most practical. Think about what your team does on a repeating basis that doesn't actually require human judgment in the moment. That's your scheduling use case. Combine it with the right integrations and a well-written skill, and you have something that genuinely saves hours.

If you're interested in how AI automation is shaping up across platforms, our coverage of HubSpot's AI Breeze Agents is worth a read — the same principles apply across tools.

Build the Agent, Then Build the Library

The biggest mindset shift for getting real value from ChatGPT Agents isn't about any single feature — it's about building a system. One agent is a tool. A library of skills shared across multiple agents, connected to your real business data, and scheduled to run when needed — that's infrastructure.

Start with one clear use case. Build it properly from blank. Write your first skill in a separate window, get it right, then save it for reuse. Add your knowledge base. Connect one integration. Then see what it does.

From there, you layer. You don't need a perfect ten-agent ecosystem on day one. You need one agent that works reliably, teaches you what good setup looks like, and gives you a skill you can carry forward.

That's the build. Not a one-sentence prompt — an intentional system, built piece by piece.

For more on how AI tools fit into a content and marketing workflow, check out our AI in Marketing blog for ongoing coverage of what's actually working.

Ready to Put AI to Work in Your Marketing?

ChatGPT Agents are available now on business plans, and the one-sentence demo doesn't do them justice. The real value is in building intentionally — starting blank, loading your knowledge base, writing reusable skills, connecting your apps, and scheduling what can run on autopilot. Done right, it's a genuine time-saver for teams running repeating workflows.

At Winsome, we help businesses figure out where AI fits — and where it doesn't — as part of a broader content and marketing strategy. If you want to think through how AI tools can support your marketing goals, let's talk.