can you build an ai agent with chatgpt?

yes, three ways. custom gpts take minutes and give you a configured assistant with tools. agent builder gives you a visual canvas for multi-step flows. the agents sdk in python or typescript gives you full control including several agents handing off to each other. the sdk is the only one without a low ceiling.

is chatgpt an agent or llm?

both, at different layers. gpt is the language model, a function that turns text into text. chatgpt is the product built around it, with memory, tools, and a user interface. in default chat that product behaves as an assistant. in agent mode and deep research it behaves as an agent.

aldena learnupdated2026-08-04

does chatgpt have a coding agent?

Q: what are the ai coding agents?

the main ones are claude code and openai codex in the terminal, github copilot coding agent and devin running asynchronously on issues, cursor and windsurf as editors, and open-source options like aider and opencode. the useful split is whether the agent works beside you or runs unattended on a task.

yes. openai's coding agent is called codex and it comes in three forms: a terminal cli, an ide extension, and a cloud version that works on a task in its own sandbox and opens a pull request. it is included with chatgpt plus, pro, and business plans, and it is separate from asking chatgpt for code in a chat.

the three forms of codex

Codex cli. Runs in your terminal against a local repository. Reads files, edits them, runs commands and tests, and iterates. This is the form most engineers use daily.

Codex ide extension. The same agent inside vs code or a jetbrains editor, so the diffs land where you already review them.

Codex cloud. You describe a task, it runs in an isolated container with your repository, and it comes back with a pull request. Nothing on your machine. Good for delegated work you check later.

All three share the model and the loop. They differ in where the code lives and how continuously you supervise.

how it differs from asking chatgpt for code

	chatgpt conversation	codex
sees your repository	only what you paste	yes, the whole thing
runs commands and tests	no	yes
edits files	no, it prints code	yes, in place
iterates on failure	you paste the error	it reads the error itself
runs unattended	no	yes, the cloud form especially

The difference is not code quality. It is that codex closes a feedback loop with the compiler and chatgpt does not.

what it is good and bad at

Good: well-specified changes in a repository it can read, refactors with test coverage, writing tests for existing code, and chasing down a failing build. Anything where the environment can tell it whether it succeeded.

Bad: large unfamiliar codebases where retrieval picks the wrong file, conventions nobody wrote down, and tasks whose specification lives in someone's head. It will not ask. It will infer a specification and finish confidently against it.

And, like every single-agent coding tool, it reviews its own work. That is fine for a twenty-minute change and it drifts on anything longer, because nothing in the loop is positioned to disagree.

how this works in aldena

Aldena's answer to the last paragraph is structural. Coding work runs as a team: a project manager splits it, engineer agents implement, and a separate reviewer reads the diff before it reaches you, arranged in an org chart.

The work happens in an isolated room with its own server, connected to github or bitbucket through skills. Agents keep memory of your default branch and conventions between runs, and nothing merges until you approve it, because the gate is on by default.

For the wider landscape, see what are the ai coding agents.

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