An AI agent is a system that receives a goal and independently decides the steps to achieve it: it queries files, uses tools, executes actions, and checks the result before proceeding. A regular chatbot answers your question and stops. An agent can, for example, read the code of a project, fix a bug, run tests, and open a pull request for you to review.
What changes from a chatbot to an agent
In a traditional chat, the conversation follows a ping-pong rhythm. You ask, the model generates text, and any real-world action is up to you: copy the code, paste it in the editor, run the command, check for errors.
With an agent, this cycle occurs within the system itself. Anthropic describes agents as systems where the language model drives the process itself and chooses which tools to use, instead of following a fixed script written by a programmer. In practice, this involves three ingredients:
- Tools: the agent can search the web, read and edit files, execute commands in the terminal, or access apps like email and calendar.
- Steps: it breaks the request into smaller parts and advances one at a time.
- Environment feedback: at each step, it looks at the real result (the output of a test, the content of a page) to decide what to do next.
The same Anthropic differentiates agents from "workflows," which are flows with defined paths in the code. If the task is predictable, like classifying emails always in the same way, a fixed flow usually suffices. The agent makes more sense when it is not possible to know in advance how many steps will be necessary, such as investigating a bug that could be anywhere in the system.
Examples of existing agents
Big AI companies already offer ready-made agents. Features change quickly, so always check the official documentation before choosing one.
ChatGPT Work, by OpenAI
Launched in July 2026, ChatGPT Work is an agent within ChatGPT designed for larger tasks. According to OpenAI, it connects to services like Slack, Microsoft Teams, Google Drive, SharePoint, email, calendar, and CRMs, and can generate spreadsheets, presentations, documents, and web applications. It also breaks a project into steps and can work on it for hours.
In the desktop application, the agent can manipulate local files, use an embedded browser, and perform actions like clicking and moving files. There are also scheduled tasks: you can ask, for example, for it to update a presentation whenever new feedback arrives via email. For programmers, OpenAI maintains Codex, its code agent, whose technology is also embedded in ChatGPT Work.
Copilot cloud agent, from GitHub
On GitHub, you can start a task for Copilot from an issue, a conversation, or the agent panel and let it work in the background. The documentation explains that the agent searches the repository, plans the changes, and implements them in a temporary development environment in the cloud. It creates the branch, makes commits, runs tests and linters, and delivers a pull request for review.
Among the uses mentioned by GitHub are adding missing tests, fixing code scanning alerts, and scheduled automations, such as triaging issues or preparing weekly release notes.
Jules, from Google
Jules is Google's asynchronous coding agent from Google Labs, which entered public beta in May 2025. It copies your repository to a virtual machine in Google Cloud, works while you do other things, and at the end, shows the plan, reasoning, and the diff of the changes. Google cites examples like writing tests, fixing bugs, creating functionalities, and updating dependency versions. You can adjust the plan before, during, and after execution.
How an agent can work for you
For those studying or working with technology, the gains appear mainly in well-defined and tedious tasks to do manually. Some realistic examples:
- Asking the agent to write tests for an old module that no one had time to cover.
- Updating a dependency and adjusting what broke with the new version.
- Fixing a security alert pointed out by the repository's code analysis tool.
- Drafting release notes from the week's pull requests.
- Gathering information scattered in documents and emails and turning it into a report or spreadsheet.
Notice a pattern: in all these cases, you remain the one who defines the goal and approves the delivery. The agent does the grunt work, and you review. For beginners, reading the diff generated by the agent also teaches a lot about the project itself.
A practical tip is to write requests as you would write a good issue. Specify what the problem is, where it appears, how to know it has been resolved, and what should not be changed. The clearer the definition of done, the less chance the agent will follow an unexpected path.
Limits and risks you need to know
Delegating autonomy to a system that acts on your behalf brings new risks. The manufacturers themselves make this clear.
Errors that accumulate
Anthropic warns that, because they act alone over many steps, agents have higher costs and run the risk of accumulating errors: a wrong decision at the beginning contaminates everything that comes after. GitHub also warns that the code generated by its agent can be incorrect or below ideal, including security vulnerabilities, and needs to be reviewed and tested.
Permissions and data access
The more apps and accounts you connect, the more the agent can do, for better or worse. If it has access to your email, it can read and send messages. Therefore, start with the minimum permissions and only grant others when necessary. In ChatGPT Work, OpenAI states that you choose what the agent can access and when it needs to request approval. In GitHub, admins can turn off the agent in specific repositories.
Security and malicious content
An agent with access to internal documents and communication tools may inadvertently share what it should not. OpenAI, for example, created a feature in ChatGPT Work called Auto-review, which uses models to review important actions and help prevent unauthorized sharing of sensitive information. Even with such layers, the rule applies: do not connect sensitive accounts unnecessarily and treat repositories with secrets, such as API keys and passwords, with extra caution.
Human supervision
Anthropic's recommendation is to test agents in isolated environments, with clear limits, and to anticipate checkpoints where a person checks the progress. Current products follow this line: GitHub's agent works in its own branch and delivers a pull request for review, keeps records of each session, and limits each execution to 59 minutes. Jules shows the plan before making changes to the code. Use these mechanisms in your favor and never approve a change that you haven’t read.
And follow Anthropic's suggestion to seek the simplest solution: if a well-written prompt solves the problem, you might not even need an agent.
Frequently asked questions
Is an AI agent the same as a chatbot?
No. The chatbot generates text responses; the agent uses tools, executes tasks in multiple steps, and takes actions, such as editing files or opening a pull request.
Do I need to know programming to use an agent?
For general-use agents, like ChatGPT Work, no. For code agents, like the one from GitHub or Jules, knowing how to program allows you to review the delivery safely.
Can I leave an agent working without supervision?
It is not recommended. The companies themselves advise reviewing what the agent produced and approving important actions before they happen.
Are agents paid?
It depends on the product and the plan, and these conditions change frequently. Check the official page of each service before deciding.
Next steps
Choose a personal or study repository, without sensitive data, and ask a code agent for a small task, like writing tests for a function. Then, read the diff line by line and note what you would change. If you want to strengthen your programming base to review this type of delivery more confidently, explore the Hcode course platform.
Sources
Building effective agents, Anthropic, December 19, 2024.
ChatGPT is now a partner for your most ambitious work, OpenAI, July 9, 2026.
GitHub Copilot on GitHub.com, GitHub Docs, accessed on October 27, 2026.
Build with Jules, your asynchronous coding agent, Google, May 20, 2025.