Agent flows with real tools
Flows are first-class. Agents can call dozens of tools across MCP, web search, web scrape, spreadsheet/CSV intelligence, sandbox execution, and graph-oriented tooling you can wire into the flow itself — including visualization/line-graph style outputs when the job is to show the shape of the data, not just narrate it.
Builders: flows and front ends
- AI flow builder — compose agent graphs instead of one-shot prompts.
- Figma Make–style UI builder — generate front ends when the deliverable is interactive, not a memo.
- Multi-asset creation in chat — drafts, tables, scripts, and UI pieces from one session.
Chat is the center; editors are the side doors
The layout is intentional: chat in the center, file/folder browsing on one side, code and editor surfaces on the other when you need them. There is a code editor and script runner, plus a sandbox shell for power users. It is not trying to win a VS Code bake-off. It is trying to help analysts and makers get to a verified output fast.
Corpus storage and workspace mounts
- Org-scoped uploads — sticky corpus files in your cloud workspace, with graph database + vector database backing for documents and CSVs.
- Cloud workspaces — open a project in a hosted sandbox and work against that tree.
- Configurable sandbox — choose which Python/NPM packages are available for analysis and builds.
Research without browser clutter
A no-frills in-app browser (no history, no bookmarks) keeps research in the same thought-stream as the agent session. Pure flow: look something up, bring the signal back into chat, keep moving.
What leaves the workspace
Org corpus lives in your cloud workspace. The primary egress is model context for the LLM calls you enable — not “your whole hard drive uploaded to a consumer chatbot.”