Rulebook AI
A rulebook AI is a set of rules that guides how coding bots work. You set it up by writing text rules that tell the bot what to do. Engineering teams use these text files to keep code styles and tool habits consistent. Without strict boundaries, bots with broad roles can run bad commands on live systems. A broad role lets an automated tool read private records or change live tables. How a rulebook AI works in your workflow Teams build a rulebook AI by placing rule files inside a project folde
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A rulebook AI is a set of rules that guides how coding bots work. You set it up by writing text rules that tell the bot what to do. Engineering teams use these text files to keep code styles and tool habits consistent. Without strict boundaries, bots with broad roles can run bad commands on live systems. A broad role lets an automated tool read private records or change live tables.
How a rulebook AI works in your workflow
Teams build a rulebook AI by placing rule files inside a project folder. The coding bot reads these text files before it writes code or runs tasks. These rules tell the bot how to name files and format database queries. They also tell the bot which local scripts it should run for testing.
To do real work, the bot also needs keys for your cloud systems. Engineers often give the bot a service role so it can read data. The bot uses this role to log in and run checks against databases. Native cloud roles check who the bot is when the session starts. Once logged in, the bot holds full power for that role until it stops.
What text rules solve and what they miss
Text rules help teams get clean code from bots without fixing every file. They stop bots from using old libraries or bad coding patterns in pull requests. Native cloud roles also make sure unknown users cannot log in to your servers. Together, text rules and cloud roles form the base of your setup.
Still, text rules are just suggestions that a language model can misread or skip. If a prompt confuses the bot, it can ignore your text rules right away. Native cloud roles only check who logs in at the start of a session. They do not check what each query or command does while it runs. A bot with a write role can drop a table if a prompt fails. It can also pull emails, phone numbers, card numbers, or national IDs into logs.
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How to add real checks to bot actions
To stay safe, teams must back up text rules with hard system checks. Do not let a bot run raw commands on live data without a review. Put an approval step in front of any command that changes or deletes data. When a bot asks to run a write query, a human should check it.
You should also add data masking to your database query results. Masking rewrites matched fields in results so emails and card numbers stay safe. This keeps private user details out of bot prompts and chat logs. By pairing text rules with live checks, your team can use bots without fear.
Govern statements and responses across databases and APIs
Good config and scoped roles are needed setup. They do not check single queries in flight or mask sensitive fields in returned rows. Across Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes APIs, SSH, and internal HTTP endpoints, hoop.dev runs beside each supported service as an open-source proxy, a sidecar for your database or API.
First, hoop.dev guardrails block banned queries before they run. Then hoop.dev data masking rewrites matched sensitive values in responses in memory. The audit trail records every statement and verdict per resource. Furthermore, hoop.dev holds SOC 2 Type II and is open source on GitHub.
Frequently asked questions
Can a coding bot ignore a rulebook AI file?
Yes, a language model treats text rules as context rather than hard code limits. If the context window fills up, the bot may skip your written rules.
Why are cloud roles not enough for AI agents?
Cloud roles only check permissions when the bot first logs in to a tool. They cannot inspect individual SQL statements or shell commands while the session runs.
How do teams protect private data from AI prompts?
Teams use dynamic data masking to rewrite sensitive fields before results reach the bot. This swaps out phone numbers, card numbers, and national IDs in query outputs.