Complete Guide to Agent Control Plane: Why Intelligence Without Governance Is A Bug

Posted on Jan 12

• Originally published at Medium on Jan 12

We are currently trying to control autonomous AI agents with “vibes.”

We write polite system prompts — “You are a helpful assistant,” “Please do not lie,” “Ensure your SQL query is safe” — and we hope the Large Language Model (LLM) honors the request. But hope is not an engineering strategy.

In the world of distributed systems, we don’t ask a microservice nicely to respect a rate limit. We enforce it at the gateway. We don’t ask a database query nicely not to drop a table. We enforce it via permissions. Yet with AI agents, we have somehow convinced ourselves that “prompt engineering” is a substitute for systems engineering.

As we move from chatbots to autonomous agents — systems that can execute code, modify data, and trigger workflows — the biggest bottleneck isn’t intelligence. It’s governance.

We need to stop treating the LLM as a magic box and start treating it as a raw compute component that requires a kernel. We need an Agent Control Plane.

My philosophy on scaling systems is simple: Scale by Subtraction. To make a complex system reliable, you don’t add features; you remove the variables that cause chaos.

In the context of Enterprise AI, the variable we need to subtract is creativity.

When I build a SQL-generating agent for a finance team, I don’t want it to be “creative.” I don’t want it to offer a witty observation about the data schema. I want it to execute a precise task: Get the data or tell me you can’t.

If I ask a SQL agent to “build me a rocket ship,” the current generation of agents will often try to be helpful. They might hallucinate a schema or offer a conversational pivot: “I can’t build rockets, but I can tell you about physics!”

Source: Dev.to