Why Your AI Agents Need Bowling Bumpers Too
When you go bowling with small children the bowling alley will either fill the gutters with inflatable bumpers, or modern places will have an automatic bumper that pops up. The purpose of the guardrails is clear—enable the child throwing the bowling ball to reach the pins without the danger of falling in a gutter. Help them be successful.
As you grow older you find the game of bowling more entertaining when the possibility of a gutter ball is introduced. It makes the game harder but more interesting. But your company’s code base, your company’s brand reputation are not a game to be trifled with. And guardrails that help your engineers hit the pins without danger of a public gutterball is why Maybe Don’t, AI exists. Guiding your people towards success is the mission, not a game.
The AI Agent Problem
Your team is already using AI agents. Cursor, Windsurf, Claude Code—they’re writing code, running commands, touching databases. They’re fast. They’re also unpredictable.
An agent doesn’t know that dropping a table is a career-ending move. It doesn’t understand that your production database isn’t a sandbox. It interprets “help me clean things up” literally. Catastrophically literally.
The Missing Layer
Traditional development guardrails assume human actors making deliberate decisions. Linters catch syntax. Code review catches logic. But AI agents operate between keystrokes—faster than review, outside your existing safety nets.
Maybe Don’t, AI sits at the chokepoint: between your AI agents and your MCP servers. Every request logged. Every dangerous operation flagged before execution. Not after. Before.
Training, Not Just Blocking
Here’s where the bowling metaphor evolves. Bumpers don’t just prevent gutter balls—they teach trajectory. When Maybe Don’t blocks an operation, it sends verbose deny messages back to the agent. “Tabs not spaces.” “PRs under 500 lines.” “Never touch production without explicit confirmation.”
The agent learns. Course-corrects. Your standards become its standards—enforced in real-time, not discovered in post-mortems.
Your Rules, Your Risk Tolerance
Generic best practices don’t account for your architecture, your compliance requirements, your definition of “dangerous.” Maybe Don’t lets you define custom policies per repo, per environment, per team. Block what matters to you. Allow what you’ve intentionally approved.
Auditable operational history means you can debug AI behavior like you debug code. You’ll know what changed, when, and by which agent. Rollback with confidence.
The Stakes Are Real
AI agents will write more of your code this year than last year. That trend line doesn’t bend. The question isn’t whether to use AI—it’s whether to use AI without guardrails.
Your engineers deserve to ship fast and sleep well. Your company deserves protection from the “DROP TABLE” moment that hasn’t happened yet.
Contact us about Maybe Don’t, AI today and put guardrails between your AI agents and your infrastructure. Because your codebase isn’t a bowling game—and gutter balls cost more than a rematch.