Custom GPT vs Agent: Which Kind of Assistant Do You Need?

A custom GPT is a configured version of ChatGPT with a persona, instructions, and optional knowledge files or actions. It answers questions, follows a defined style, and can call a limited set of APIs you specify. An agent — in the current OpenAI sense, a ChatGPT Work agent — uses tools to plan and execute multi-step tasks: browsing the web, writing and running code, reading and creating files, and using connected apps. The distinction is between a configured assistant and an autonomous task runner. Tools like fenbs hold shared project instructions and context across whichever assistant you use, so the work survives when you switch.

Choosing the wrong type for a task produces predictable problems. A custom GPT asked to "handle my weekly report" cannot act on the calendar, fetch fresh data, or send the result — it can only talk about those things. An agent asked to serve as a company knowledge base will use its tools to try to answer questions rather than staying in a defined conversational scope. Matching the type to the task prevents both.

What a custom GPT does well

A custom GPT is a conversational assistant with a defined purpose and persona. You write the instructions, add any knowledge files the assistant should know about, and optionally configure API actions it can call. It is excellent for a company support bot, a specialized advisor with a consistent tone, or a knowledge base where users ask questions about a defined document set.

Custom GPTs are persistent configurations: once built, anyone with access can start a conversation and get the same defined assistant. The limitation is that they cannot act autonomously across multiple steps. They wait for user input, respond, and wait again. For work that requires planning and acting without a person driving every step, a custom GPT is the wrong tool.

Note: OpenAI has announced that custom GPTs will retire on December 11, 2026. Check the OpenAI help center for current status and alternatives before investing in a new custom GPT build.

What an agent does well

A ChatGPT Work agent takes a task description and executes it. It can browse the web for current information, write and run code to process data, read and create files, and use the connected apps in your ChatGPT Work environment. It plans the steps, carries them out, and returns the result — or stops at a defined checkpoint for human review.

Agents are well suited to tasks that have defined inputs and outputs but require multiple steps to complete: "Summarize this week's sales data, compare it to last week, and create a draft report in the shared folder." A person describes the task; the agent handles the execution. The person reviews the result.

Related reading: Claude Projects vs. custom GPTs and ChatGPT Projects vs. custom GPTs compare different kinds of persistent configurations.

When to choose a custom GPT

• You need a persistent assistant with a defined persona and knowledge base for conversational use.
• Multiple people need to access the same configured assistant without building it themselves.
• The task is answering questions from a document set, not acting on a workflow.
• You want a customer-facing or team-facing assistant with a consistent tone and scope.
• The work is conversation-driven, not step-driven.

When to choose an agent

• The task has multiple steps that depend on each other and require real-world actions.
• You need the assistant to fetch current information, process data, or create files without you driving every step.
• The task can be described as "do X, then Y, then Z and give me the result."
• You are willing to design and test a task brief rather than rely on conversation to steer the work.
• A human review checkpoint at the end is acceptable before the result is used.

Using both together

Some workflows use both. A custom GPT (or its successor) handles customer conversations and captures requests; an agent processes those requests, retrieves data, and builds the output. The conversational layer keeps humans in the loop; the execution layer handles the mechanical steps. The key is deciding which tool owns which part and where the handoff happens.

The handoff is where shared context matters most. If the custom GPT captures a decision and the agent needs to act on it, that decision needs to exist somewhere both can read. A shared record outside both tools is the most reliable way to do that.

How to do this with fenbs

1. Create a free account and add a project for the work this assistant supports.
2. Write the standing instructions and approval rules as Decisions and rules in the project.
3. Connect Claude, ChatGPT, or another supported app from Integrations and ask it to read the project context before each session.
4. When you switch between a custom GPT session and an agent task, save where you left off in fenbs so the next session starts from the current state.
5. When a new team member sets up the same assistant, share the project so they start from the same instructions.