How to Build a Self-Updating Work Brain With Town

A self-updating work brain with Town is a connected workspace where Slack requests, selected work sources, a visible Wiki, and recurring routines reinforce one another. The source guide from The Rundown University describes a five-step setup: connect Slack, choose sources, inspect Town's Wiki, create a daily review habit, and improve routines one at a time.

Definition: Town's work-brain setup is a reviewed context-and-routine system built around Slack and the sources a user explicitly connects.

Example: A team can ask Town for a status check in Slack, review the response, and collect recurring AI reports in a calendar block for daily inspection.

Key takeaway: The system becomes useful through controlled context and repeated review, not by enabling every automation during onboarding.

Business impact: A visible review loop gives recurring AI output a place to be checked before it becomes an approved action or a trusted part of daily work.

What should a Town work brain contain?

A useful Town work brain has four parts: context, an interface, routines, and review. Context comes from the Slack workspace and selected connections; the browser Wiki makes the resulting profile and work knowledge visible; routines produce recurring outputs; and review catches wrong assumptions before they drive an action. This structure is useful for operators who want repeatable AI assistance without building or hosting an agent themselves.

The design is deliberately narrower than “connect everything.” Town is the named service in this guide, but the broader principle also applies to any AI agent connected to business tools: start with one workflow, restrict permissions, and make outputs auditable before expanding access. For the underlying distinction between assistants and action-taking systems, see what an AI agent is.

Step 1: Connect Town to Slack and start small

Town's first setup step is to create an account, connect the Slack workspace where it will be used, and choose a small starter set of routines. The source guide names Auto-inbox, Morning Briefing, and Meeting Briefing as examples shown during onboarding. Begin with the routine closest to work you already do, because a familiar process gives you a concrete output to judge instead of an abstract promise.

Town's routine settings matter because a routine can contain multiple actions. Auto-inbox, for example, may expose separate options for labeling mail, assisting with scheduling, or drafting replies. Review those options before enabling them, then leave unnecessary actions off. A narrow first routine is easier to inspect, pause, and improve than a bundle of automations with unclear side effects.

Step 2: Give Town only the context it needs

Town's connection step should be treated as a permission decision, not a checklist. The guide lists Notion, Google, Granola, and Calendly as optional sources, but the correct connection set depends on the work you want Town to support. Add only the pages or accounts needed for that workflow, and leave private messages or unrelated sources disconnected.

The practical rule is to separate context access from action authority. A source connection lets Town use information; it does not prove that every resulting briefing or recommendation is accurate. That distinction matches the safer rollout pattern described in our guide to connecting AI agents to CRM, Google Sheets, and Slack: begin with narrow read access, add one low-risk action later, and keep a record of what the system did.

Step 3: Check the Wiki before trusting the output

Town's Wiki is the review surface for the context it has assembled. Open the browser dashboard and inspect the sections Town creates for your overview, goals, projects, profile, notes, and people. Start with the facts most likely to change a routine's result: current projects, responsibilities, communication preferences, and working relationships.

A Town Wiki is ready for a routine only when its relevant sections are accurate enough for the task. If a project is missing, a responsibility is stale, or a profile detail is wrong, treat that section as unfinished context rather than silently filling the gap yourself. This is the same operational discipline that makes internal knowledge search a viable first automation: the source set must be trusted before an AI layer can reliably retrieve from it.

Step 4: Use Slack as the request surface and add a review block

Town's Slack workflow starts with a low-risk question in the town-square channel, tagging @Town when requesting a status check. A useful first request asks what tasks are active and whether any blockers exist. Review that answer before asking Town to create a calendar event, update another tool, or perform any other external action.

The source guide then uses a daily 45-minute review window as the control point for recurring output. Town is asked to find a time, reserve the recurring block, gather reports from AI-assisted research and operational work, and link those reports in the calendar event description. Approve only that requested calendar action, then verify the recurring time and the links it created.

This review block is more important than the calendar format. It turns scattered reports into a repeatable inspection queue, while keeping the human responsible for deciding whether an output is useful or an action is safe. Town may also need approval the first time it performs an action in Slack, so an approved request is part of the setup rather than an exception to it.

Step 5: Improve one Town routine at a time

Town's routine library should be treated as a shortlist, not a mandate to activate every available workflow. Open Routines, see what is already running, and ask Town for a small recommendation based on the context you just reviewed. Choose one suggestion, inspect what Town will read and do, and test it with a low-risk task.

Town's routine earns expansion only after one output survives the daily review. The source guide's final step says to inspect a routine, test it with a low-risk task, and review whether the result is useful. Keep the routine only when Town produces a repeatable benefit; otherwise flag the error, narrow the job, or remove it before adding another.

A safe operating loop for Town

Town's setup can be summarized as connect → inspect → request → review → refine. Connect the smallest useful source set, inspect the Wiki's context, request a low-risk output in Slack, review the result during a dedicated block, and refine one routine before adding another. The loop is useful because each stage creates evidence for the next decision.

Keep external actions behind approval until the scope is clear. The source guide specifically recommends understanding what Town reads, changes, or sends before allowing more autonomous actions. For broader planning, start with work that repeats, has clear inputs and outputs, and can be checked without expensive mistakes.

What to watch in Town after the first week

After a week, inspect Town's Wiki, one Town routine's output, and the Town review block as three separate parts of the system. The source guide recommends repeating that review loop, checking the Wiki context, flagging what is wrong, and keeping only what saves time. If Town's Wiki is inaccurate, fix the context before changing the prompt; if the output is weak, narrow the routine; if the output is useful but never reviewed, improve the schedule or reduce the routine count.

Town's durable value in this guide is therefore not the number of automations it can expose. It is the combination of visible context, explicit permissions, a Slack request surface, and a repeated human review habit. That is the operating model to preserve as the system grows.

Need help scoping a safe first automation workflow? Get in touch with Yowox.