1Presence vs Custom GPTs.

GPTs are toys you configure in a panel. Agents are colleagues you talk to.

A Custom GPT is a system prompt plus a small set of optional "actions" — webhook endpoints the model can call. You build one by filling in a form: name, description, instructions, suggested prompts, conversation starters, knowledge files. Some of them are good. Most are very obviously a system prompt with a colourful icon.

GPTs are largely stateless. Any memory they have is unstructured and tied to your single ChatGPT profile, not to the specific GPT — there is no browsable, per-agent knowledge that compounds. They have no access to your own services unless you specifically wire up an action, and that wiring is a developer task, not a conversation. And whether a GPT is public or private, it runs on shared infrastructure rather than as an isolated entity that is yours alone.

An agent in 1Presence is a private entity with its own identity, voice, skills, connectors, memory, and vault workspace. Created by talking — describe what you need, and 1Presence shapes it for you. Tied to your account; nobody else uses your agent.

Agents reach into your real tools — your Gmail, your calendar, your Notion — and they accumulate working memory over time. They can run on a schedule. They can pause and ask you a question. They can hand off to other agents in a workflow.

The full breakdown is below.

Actions are the part people assume close the gap, so they are worth understanding properly. An Action lets a Custom GPT call an external API — but you give it that ability by supplying an OpenAPI schema for the endpoint, configuring the authentication, and handling the auth callback. In other words, connecting a GPT to a service is a small integration project, not a setting. If there is no ready-made Action for the tool you use, you are writing one.

Two limits bite even once an Action works. They tend to be read-mostly and one-at-a-time: a GPT will happily fetch from a single endpoint, but it does not synthesise across your inbox, your calendar and your notes in one move — the thing you actually want when you ask to be briefed for a meeting. And the connection is generic, not personal: an Action knows how to call an API; it does not carry a memory of how you work, what you decided last week, or who the people in the thread are.

1Presence starts from the other end. The connectors are built in and granted on first use — no schema, no callback, no developer afternoon — and the agent reads across them at once, against a memory that is genuinely yours. The question “brief me for the 3pm” is a single sentence here; with Actions it is an integration plus a lot of hoping.

Here is the gap in one exchange. The request reaches across three services at once, weighs them against what the agent already knows, and comes back with a judgement — not a single API response.

A Custom GPT with a calendar Action can fetch the event. It cannot, in the same breath, check whether the blocker cleared in your inbox, weigh it against a deadline it remembers, and give you a recommendation. That synthesis — across tools, against memory — is the whole point of an agent, and it is what Actions, one endpoint at a time, are not built to do.

Building a Custom GPT means opening the builder and filling in a form — name, description, a block of instructions, conversation starters, knowledge files — then, if you want it to do anything beyond talk, configuring Actions. It is approachable for the prompt-and-instructions part and distinctly developer-shaped for the rest. Building a 1Presence agent means describing what you want in plain language and letting it be shaped in conversation; there is no panel and no schema.

On price, the comparison is easy to miss: Custom GPTs live inside ChatGPT, so building and using them sits behind a $20/month ChatGPT Plus subscription. 1Presence is $8/month with $5 of usage credit included, and your agents — with memory, connectors and scheduling — are part of it, not a tier above.

Custom GPTs are the closest thing the major chat products offer to "specialist agents," and they end up being clearly demoware — fun to make, brittle in practice, forgotten after a week. The reason is not that the AI is incapable. It is that the surrounding infrastructure (memory, connectors, scheduling, isolation) is missing.

1Presence is built around the assumption that an agent should feel like a real colleague: it knows you, it can reach the things you can reach, it shows up at the right time, it gets better the longer you work together. The AI alone cannot do that. The system around it has to.