Distributed Negative Feedback Optimization for Multi-Agent AI

Existing AI safety research focuses predominantly on controlling the outputs of individual agents through guardrails, alignment training, and constitutional constraints. We argue that this single-agent paradigm has a fundamental blind spot: an agent cannot reliably detect errors in its own reasoning, just as an open-loop control system cannot correct disturbances it does not measure. We propose Distributed Negative Feedback Optimization (DNFO), a framework that applies the century-old principle of negative feedback from control engineering to multi-agent AI systems. DNFO recasts multi-agent collaboration as a closed-loop control problem, where each agent's output is structurally verified by independent…