A guided walkthrough of the basics in Tana, the agentic meeting platform, and how the pieces fit together. You run a meeting, watch the AI draft proposals from what was said and shown, review and accept the ones you want, send that work to the tools you already use, and organize everything with spaces and types so your team can find it later. It is the fastest way to see the whole loop end to end.
What Tana is [0:00]
Tana brings AI and agents into the meeting itself, so you complete work as you talk instead of leaving with notes to process later. It sees both what is said and what is shared on screen, and closes the gap between the conversation and execution: you come out with work that is more or less ready to go, shipped to where work actually happens. See Getting started for the same idea in writing.
Spaces [3:14]
A space is a container for related work, like a folder or a project, with its own access control: everyone in the org, just you, or a named few. Everything you put in it inherits those permissions. You can also @-mention a whole space in a chat to use it as reusable context, which is what sets Tana apart from a standalone AI tool.
Types [4:22]
A type (the equivalent of a supertag in Tana Outliner) says what something is: a bug, a dev task, a person. That helps the AI treat it correctly, gives it a template of fields like severity, and lets you link types together, for example an issue linked to a product area. Track issues with a type goes deeper.
Inside a finished meeting [7:28]
The walkthrough opens just after a meeting, in the after-meeting view: a digest with a title and description, the full raw transcript behind it, and the sections Tana draws out live while you speak. Everything here is collaborative with the people in your org from the start.
The summary, and sharing [8:31]
Tana writes a rich summary automatically. Every item carries its own access control, not just spaces, so you can hand a single summary to a customer with a guest link, or keep it to selected people, without changing anything else.
Wrap-up and proposals [10:09]
When the meeting ends, the wrap-up writes the summary, makes sure the digest is accurate, and drafts proposals for the actions that came up. In the demo that includes updating an existing issue with new customer-discovery context. You review each one and decide what lands, so everyone with access gets the richer context at once.
Send work to your tools [11:17]
From a proposal you can send the work, with its context, to Linear, Jira, or GitHub, or hand it to a coding agent like Claude Code that spins up a session and starts on the issue. You set these up once in Settings, then Integrations. See Connect GitHub, Connect Linear, and Integrations.
Working during the meeting [13:22]
Live, you have your call tiles, the live digest, and pinned talking points. The + menu is where you capture as you go: search for what you discussed and capture it from the last couple of minutes. Context is not just the transcript, if someone is sharing their screen, Tana takes that into account too, so the outcomes are richer.
Every proposal has a type [15:35]
Each proposal is labeled with its type, issue, feature request, company, deal, and the AI considers what kind of thing it is while extracting. Because types carry fields and can have a skill attached (say, one that always sends issues to GitHub), this is what makes more powerful workflows possible.
The Library [16:50]
Everything you create in Tana lives in the Library: documents, tasks, artifacts, types, chats. Spaces are containers inside it, and they are where access control lives, the product space shared with the whole org, a research space limited to a few people.
Browse and filter [19:05]
The Library is built for browsing. Filters let you see just meetings (today, upcoming, shared, or with a specific person), or switch to chats, canvases, agents, skills, artifacts, spaces, and types. Organize your context covers the model.
Today, and the chat bar [21:00]
Today shows your meetings and a chat bar. The chat AI has the same tools you do, plus current knowledge of Tana itself, so it can both do the work and explain how the app works.
From a feature request to a PRD [21:55]
The session ends by running a skill, reusable instructions you save and rerun, to turn a captured customer feature request into a full product spec, by voice. Because the feature request holds the customer's own words and quotes, the spec links back to the source and flags open questions, shortening the path from a customer conversation to built product.