> For the complete documentation index, see [llms.txt](https://docs.quantumbyte.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.quantumbyte.ai/components/development-service/ai-dev-agent.md).

# AI Dev Agent

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Available in v3.1.0
{% endhint %}

The AI Dev Agent is an automated development assistant that executes the steps defined in the Roadmap. It can generate, edit, and deploy code based on the planned milestones created in the Roadmap. By interpreting the structure and logic of the Canvas, the AI Dev Agent ensures that each technical implementation aligns with the user’s goals and business logic.

This agent operates continuously in the background, managing dependencies, validating outputs, and ensuring that the system remains coherent as updates occur. Users can monitor progress or make adjustments directly within the platform without having to handle code manually. The AI Dev Agent significantly reduces development time and allows users to iterate faster from concept to functional product.


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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.quantumbyte.ai/components/development-service/ai-dev-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.