AI Agent with ChatGPT: How to Create Custom GPTs [2026]

ChatGPT is the most accessible platform on the market for creating an AI agent without writing a single line of code. OpenAI's custom GPTs allow you to define behavioral instructions, upload documents as a knowledge base, and connect external APIs through actions. The result is an assistant that goes beyond answering generic questions: it works with your data and executes specific tasks. In this guide we build one step by step, with its real capabilities and its limitations without sugar-coating. If you need a global view of the ecosystem, start with our complete guide to AI agents.

What Are Custom GPTs?

OpenAI launched custom GPTs as a configuration layer on top of ChatGPT that allows any user to create tailored assistants. A custom GPT consists of three elements: a system prompt with behavioral instructions (who it is, how it responds, what it can and cannot do), uploaded documents that function as a knowledge base through RAG (Retrieval-Augmented Generation), and actions that connect the GPT with external APIs to execute real operations.

GPTs are available in both the free plan and paid plans of ChatGPT, although with important differences in usage limits. OpenAI also offers the GPT Store to distribute GPTs publicly and the Operator functionality for automated web browsing tasks.

The key distinction: a custom GPT is not exactly a complete AI agent. It lacks persistent memory between sessions, cannot orchestrate multiple tools autonomously, and does not manage complex conditional flows. It's a powerful middle ground between a saved prompt and a production agent. To understand the architectural differences in depth, check out what is an AI agent.

How to Create a GPT Step by Step

1. Access the Builder

Open ChatGPT, click on "Explore GPTs" in the sidebar and select "Create". The Builder opens, an interface divided into two panels: configuration on the left and real-time preview on the right. Everything is configured from here, without leaving the browser.

2. Define the Instructions

The system prompt is the DNA of the GPT. Here you define its role, tone, behavioral rules, and the limits of what it can do. The quality of these instructions directly determines the quality of the responses.

Be specific. Instead of writing "you are a support assistant", write: "You are the support agent for [company]. You respond exclusively about products from the 2026 catalog. If the user asks about topics outside your scope, you indicate that you cannot help and offer contact with the human team. Professional tone, concise responses, maximum 3 paragraphs." This level of detail is applied prompt engineering: the more precise the prompt, the fewer hallucinations and off-context responses.

3. Upload Your Knowledge Base

In the configuration tab, upload PDF files, text documents, spreadsheets, or presentations. The GPT uses RAG to search for relevant information within those files when the user asks a question. Current limits: 20 files with a maximum of 512 MB in total.

Immediate use cases: product documentation, price catalogs, internal FAQs, procedure manuals, and company policies. The GPT does not memorize complete documents; it searches for relevant fragments by semantic similarity, which means the quality of the response depends on the structure and clarity of your documents.

4. Configure Actions (APIs)

Actions are what turn a custom GPT into something more than a chatbot with instructions. They allow you to connect the GPT with external systems to query data in real-time or execute operations: search orders in your CRM, verify stock availability, register leads, or send notifications.

To configure an action you need your API schema in OpenAPI (Swagger) format. You enter the server URL, paste the specification, and the GPT automatically learns what endpoints are available, what parameters they accept, and what data they return. If your company already has documented APIs, the process takes minutes. If not, this is where you need a developer.

5. Test and Publish

Use the preview panel to test real conversations before publishing. Verify that the instructions work, that the knowledge base returns accurate information, and that the actions execute correctly. Three publishing modes: private (only you), by link (anyone with the link), or in the GPT Store (public and discoverable).

Limitations of GPTs as AI Agents

Transparency here matters more than enthusiasm. Custom GPTs have real limitations you need to know before basing your operation on them.

There is no persistent memory between sessions. Each new conversation starts from scratch. The GPT does not remember that a customer contacted yesterday about the same problem nor accumulates context between interactions. This disqualifies it for support cases where customer history is critical.

Actions with external APIs are functional but unstable. Authentication can fail, timeouts are frequent with slow APIs, and debugging when something doesn't work is limited: there are no detailed logs or reasoning traceability.

The GPT only lives within the ChatGPT interface. You cannot deploy it on WhatsApp, Telegram, your website, or any other channel without going through OpenAI's API, which has its own cost and requires additional development. There is no analytics panel to measure performance, resolution rates, or user satisfaction. And there is no multi-agent orchestration capability: a GPT works alone, without coordination with other specialized agents.

For enterprise use at scale, specialized platforms like GuruSup offer what GPTs cannot: deployment on WhatsApp with the WhatsApp Business API, persistent customer memory, pre-configured CRM integrations, and real-time performance analytics.

When to Use ChatGPT GPTs vs Other Platforms?

ChatGPT GPTs are the best option when you need a quick prototype, a personal use assistant, a Q&A system about internal documents, or a tool for a small team already using ChatGPT. The entry cost is zero and the learning curve is minimal.

When you need complex automations with conditional logic, integrations with multiple systems, and deployment on your own channels, n8n or LangChain are more suitable. We detail the process in our guide on AI agent with n8n. If you're looking for no-cost options to experiment, we have a complete comparison in free AI agent.

And when the goal is customer support on WhatsApp at scale, with persistent memory, CRM integrations, and business metrics, a platform like GuruSup is specifically designed for that. See how an AI agent for WhatsApp works.

Conclusion

ChatGPT's custom GPTs are the best entry point for creating an AI agent without code: instructions, documents, and actions in a visual interface anyone can use. Their limitations appear when you need memory, multi-channel, or scale, but as a prototyping and concept validation tool they have no rival in accessibility. Check our complete guide to AI agents for global context, learn to create an AI agent with any platform, or discover the free options available in 2026.

GuruSup takes your agent beyond the prototype. Deploy AI agents on WhatsApp with persistent customer memory, direct CRM integration, and performance analytics. No channel limitations, no conversation reset. Try GuruSup for free and move from the test GPT to the agent that solves in production.