Why Cognitive Robotics Matters: Lessons from OntoAgent and LLM Deployment in HARMONIC for Safety-Critical Robot Teaming

Abstract

Robots operating alongside humans must recognize what they do not know before acting, diagnose problems from domain knowledge, and reason about action consequences. These capabilities are operational requirements, not optimization targets, and their absence produces silent and unrecoverable failures. We present a first-of-its-kind controlled comparison between OntoAgent, our content-centric cognitive architecture, and six LLMs spanning frontier and efficient tiers as drop-in replacements at the strategic layer of the same robotic system in HARMONIC. LLMs fail to verify their knowledge state before acting, even when given equivalent procedural knowledge. The deficit is architectural, not knowledge-based. Knowledge-grounded architectures must retain decision authority; LLMs contribute where their strengths apply.