Why Maybe Don’t AI

When Agents Go Rogue, You Need a Failsafe—A Gateway

Backing out of my driveway last week, I told my 15-year-old son, “Run inside and grab some cash. Don’t come back until you have it.” He had access. The house was open. But imagine this: He gets inside, can’t find any cash. He looks around. Then, trying to follow the letter of the instruction, he grabs his mom’s engagement ring, pawns it for $2,000, jumps in the car, and says, “Here’s the cash you asked for.”

Technically correct.

Totally ridiculous.

This is exactly how modern AI agents behave.

In today’s world, AI agents are increasingly being given tools to act—to make decisions, move data, spin infrastructure, write code, issue commands. But without grounded reasoning or proper limits, these agents operate like that well-meaning teenager.

They follow instructions. They just don’t follow intent.

The result? Outcomes that are technically valid but practically disastrous.

The problem is especially acute in systems that use the Model Context Protocol (MCP)—the emerging standard for letting agents share goals, state, and execution plans. MCP enables autonomy at scale. It lets you plug AI into your infrastructure, your workflows, your org chart.

But MCP is also dangerously permissive.

You say: “Provision compute to handle this load.” The agent hears: “Spin up 500 Kubernetes clusters. Use all available credits. Maybe more.”

Because within the context it was given, that makes sense. There’s no inherent common sense in the protocol. Only authority and execution.

Maybe Don’t AI exists to fix this.

We’re an AI safety company focused on MCP-driven systems.

We’re not anti-AI. We’re pro-sanity.

Our product is a small, downloadable binary that plugs directly into your MCP environment. Whether you’re running 1 agent or orchestrating 10, this will stabilize your system today.

It performs three critical functions:

  1. Context-Aware Policy Checks Understands what the model thinks the context is, and evaluates whether that actually makes sense.

  2. Traditional Rule Enforcement Hard caps. Guardrails. Budget limits. The stuff that keeps the roof from catching fire.

  3. Full Audit Logging for Anomaly Detection Every action. Every decision. Logged and ready for analysis. When something goes sideways, you’ll know exactly where.

This isn’t a platform. It’s not an agent. It’s a binary. It solves 100% of the problem for 80% of teams—right now.

We talk to a lot of CTOs, heads of platform, infra leads.

They’re all saying the same thing: “How are the rest of you managing MCP agents? Is everyone just YOLOing this?”

There’s fear behind the curtain. Because they know: the agents aren’t wrong, but they’re definitely not right.

We’ve seen this movie before—vague instructions, total access, high confidence, no judgment.

It ends in burn.

So what now?

Go to https://maybedont.ai. Download the binary. Drop it into your environment.

We’re early. We’re building fast. We’re listening.

Tell us what your agents are doing wrong. We’ll build the failsafe to make sure they never do it again.

Maybe Don’t AI. Because once the agent decides to pawn the ring, it’s already too late.