Many AI tools are built into the system in functionality bundles called AI Tool Libraries. There are different kinds of libraries:
Standard libraries: these are a default set of AI tool libraries with capabilities like web search and browsing, email drafting, image and video processing, and document processing.
Web search: tools to search the web, answer questions based on web content, read and extract information from web pages
File system module: tools to read, create, update, move, and delete files in a cloud file system (Google Drive or Office 365, depending on the user's work environment).
Image processing: tools to edit and query image content
Video processing: tools to edit and query video content
Communications: tools to draft or send email and messages
Document templating: tools to create documents based on parameterized templates
Application libraries: every user can augment their account with connections to other systems. For example, a user may add a Google Drive connection and this automatically enables an AI tool library that allows that user's AI agents to read and write files and spreadsheets and folders in that Google Drive. Another common use of connections is to integrate with an existing enterprise application using the MCP protocol. Learn more about integration with applications.
Imported thunk libraries: users can define AI tools in one thunk and share them across many other thunks (of their own or for access to other users as well). There are many benefits, especially when teams of users are implementing many AI agent automation processes. Learn more about modular reuse of thunks.
Platform libraries: some of the internal capabilities of the platform (eg: dynamic planning, or reflection, or workflow state update) are also represented as AI tools. While the user cannot directly author these tools, they can be configured in the same way as any other AI tool.