> For the complete documentation index, see [llms.txt](https://docs.tabnine.com/main/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tabnine.com/main/getting-started/tabnine-agent.md).

# Tabnine Agent

## Introduction

**Tabnine Agent** extends the capabilities of Tabnine beyond inline code completion and conversational chat by introducing an **autonomous, task-oriented AI assistant** that can act directly within the developer’s environment.

Unlike Tabnine Chat, which provides on-demand natural language conversations with underlying models (such as explaining code, generating snippets, or answering documentation queries), **Tabnine Agent** **operates autonomously to achieve the user’s specified goal** and can perform higher-order development tasks. These tasks can include codebase-wide refactoring, automated test generation, documentation synthesis, and policy validation.

### What is Tabnine Agent?

Tabnine Agent can be seen as an initiative-enhanced version of Tabnine Chat. On top of that, Tabnine Agent adds and explains more of its reasoning to the user.

Unlike other agentic approaches, Tabnine Agent maintains a tight feedback loop with the developer. It independently determines when to check in for input or approval, such as requesting to *Proceed* with a complex agentic workflow.

This ensures that the agent remains aligned with user intent and minimizes introducing redundant or conflicting code within large, enterprise codebases.

Agent will take into account the current state of a project as well as context to make its decisions. It will in turn respond to state changes, take dependencies into account, break down complex tasks for better processing (and clearer explanations to users), and respond to user feedback to make project code edits.

<figure><img src="/files/TbwiHWa3UTiOaWZZDkbT" alt=""><figcaption></figcaption></figure>


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.tabnine.com/main/getting-started/tabnine-agent.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.