The Deterministic Control Plane: Building Reliable AI Agents

The Deterministic Control Plane: Building Reliable AI Agents That Don't Surprise You "AI is not trustworthy. Go back to coding by hand." — This sentiment captures a growing tension in software engineering. But what if the problem isn't AI itself, but the missing control systems? In my experience running autonomous AI agents in production, I've learned one fundamental truth: probabilistic AI needs deterministic guardrails . Here's how to build them. The Core Problem AI agents are probabilistic by nature. They generate outputs based on probability distributions, not hardcoded logic. This is their strength—and their weakness. When an AI agent decides to execute a tool with wrong parameters, misinterpret a user's intent, or choose an inappropriate action sequence—you can't simply "debug" it like traditional software. You need control systems . The Three Reliability Modes I Use Based on running agents in production, I've identified three modes of agent reliability: 1. Supervised Autonomy (L