Google Chat AI Bot: Plan a Useful Assistant for Your Team

A Google Chat AI bot is a workspace app that responds to messages, reacts to slash commands, and can connect to outside services. It can answer team questions, look up data, or kick off a workflow — all from inside a Chat conversation. Tools like fenbs keep the team's agreed bot instructions and project rules in a shared record outside chat, so they are available to every connected AI assistant and survive a membership change.

Building a useful bot means making a few clear setup choices upfront: what the bot will and won't do, who can add it to spaces, and how the instructions are maintained. Getting those right saves significant rework after launch.

What a Google Chat bot can do

A Google Chat app can respond to direct messages, respond when @mentioned in a space, handle slash commands, and post cards with structured information. More capable bots use Dialogflow CX or a connected LLM to interpret natural language. Simpler ones respond to a defined set of slash commands and keywords.

Google also provides Gemini-powered features in Chat directly — smart replies, meeting summaries, and space summaries — but these are Google's own features, not custom bots you build. The distinction matters: a custom bot gives you control over the logic and the data it accesses. Google's built-in features operate on Google's own model with your workspace data.

Setup choices and permissions

To build a Google Chat app, you need a Google Cloud project and access to the Google Chat API. The setup involves:

1. Creating a Google Cloud project at console.cloud.google.com↗ (opens in a new tab) and enabling the Google Chat API.
2. Choosing a connection type: HTTP (your app runs on a server and Google sends events as webhooks) or Apps Script (your code runs on Google's infrastructure, simpler for straightforward bots).
3. Writing the event handler: the code that runs when the bot receives a message or command.
4. Configuring the bot in the Chat API settings: its name, description, avatar, and which events it receives.
5. Publishing it internally to your workspace or requesting external verification from Google if you want it available to other organizations.

Workspace admins control which Chat apps users can install. If your organization restricts app installation to admins, you need admin approval before anyone can add the bot to a space. Check Google's current documentation↗ (opens in a new tab) for the latest requirements.

Writing instructions the bot follows reliably

If your bot uses a language model, the system prompt is the most important thing you write. It defines the bot's scope, tone, and constraints. A system prompt like "you are a helpful assistant for our team" is too vague. A system prompt like "you answer questions about our project status by reading from the linked Notion space; if you don't know, say so and offer to connect the person with a team member" is specific enough to produce consistent results.

Write the instructions as if someone unfamiliar with your team will run the bot. Include: what the bot is for, what it should not do, how it should respond when it does not know the answer, and any topics that always require a human.

fenbs keeps those instructions and the decisions behind them in Decisions and rules — a record every connected assistant reads before starting work. If the team decides that the bot must never answer questions about personnel matters, recording that rule means it survives a system prompt rewrite or a new team member setting up the next version.

Testing before adding it to the team

Test the bot in a private space or a test workspace before adding it to a team space. Test the happy path (a question it should answer well), the edge case (a question at the edge of its scope), and the failure case (a question it should decline or escalate).

Share the test space with one or two teammates first. The questions they ask are more realistic than the ones you tested yourself. Watch for answers that are technically correct but confidently wrong about your team's specific context — that is usually a signal that the instructions need more specificity.

Keeping agreed instructions outside chat

Bot instructions saved only in a system prompt or a script file are easy to lose track of. When someone updates the prompt, there is often no record of what changed or why. When the original developer leaves, there is no reference for the next person.

For a broader view of what Google's AI tools can do, see Google AI tools. For how AI can play a role alongside people in a team, see AI employees. For how to bring AI into your daily work more broadly, see how to use AI at work.

How to manage your Google Chat bot's rules with fenbs

1. Create a free account and add a project for the bot you are building.
2. Add the bot's scope — what it does and does not do — as a task with the acceptance criteria in the plan.
3. Write the standing instructions and constraints as Decisions and rules. Include who decided them and the reason.
4. Connect your AI assistant over MCP from Integrations. Ask it to read the project context before drafting or revising the system prompt.
5. After launch, add follow-up improvements as Enhancement tasks. Record any changes to the scope rules when they happen.

fenbs does not integrate natively with Google Chat. It works alongside it: connect an AI assistant to fenbs and use that assistant to read project context and rules before making changes to the bot. The bot's instructions live in Google's infrastructure; the reasoning behind them lives in fenbs.