Agentation is a developer tool designed to enhance the efficiency of AI coding agents by providing structured, precise feedback on web elements. It addresses the common challenge of vaguely describing user interface components to AI models, enabling agents to locate and modify corresponding source code with significantly greater accuracy and speed.

The core methodology of Agentation revolves around bridging the visual representation of a web application with its underlying codebase. When a user identifies a bug or desires a change in a running application, they activate Agentation within their browser. The tool allows for direct interaction with the webpage elements: users can click on specific elements, select text regions, and then append textual notes describing the required feedback or issue.

Upon interaction, Agentation programmatically captures critical identifying attributes of the selected web element. These attributes include, but are not limited to, CSS class names (e.g., .primary-button), CSS selectors (e.g., $.sidebar > .nav-actions > button.primary$), and potentially positional data within the Document Object Model (DOM) or visual layout. This information forms the technical foundation for the AI agent's subsequent code search.

Following the annotation, Agentation generates a structured output, typically in markdown format. This output meticulously combines the captured technical identifiers of the element with the user's descriptive feedback. For instance, instead of a vague instruction like "fix the blue button in the sidebar," the output might provide:

This structured markdown is then copied by the user and directly pasted into an AI coding agent environment, such as Claude Code, Cursor, or similar tools that possess access to the codebase. The AI agent, leveraging its understanding of code structures and its ability to search across files, utilizes the provided class names and selectors. It performs targeted searches (e.g., grep commands or semantic code navigation) within the codebase to identify the specific component, file, or line of code responsible for rendering the annotated UI element. By precisely knowing *which* element is being referred to through its unique selectors, the agent can swiftly pinpoint the relevant source code, significantly reducing the iterative cycles typically required for diagnosis and correction.

The tool operates on the key insight that detailed, machine-readable element identification is paramount for AI agents to perform effective code modifications. It is designed to be agent-agnostic, meaning its markdown output is universally compatible with any AI tool capable of processing text and accessing a codebase. Agentation has zero external dependencies beyond React, emphasizing its lightweight and integrable nature. Best practices for its use include being specific in feedback, addressing one issue per annotation, providing context, utilizing text selection for content-specific issues, and pausing animations for stable element annotation.