Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design
Zeda Xu, Nikolas Martelaro, Christopher McComb

TL;DR
This paper introduces a metacognitive co-regulation loop for agentic AI in engineering design, improving design quality and exploration efficiency by mitigating fixation issues.
Contribution
It proposes a novel self-regulation and co-regulation loop architecture that enhances AI design agents' performance and exploration capabilities.
Findings
CRDAL outperforms SRL and RWL in design quality.
CRDAL navigates the design space more effectively.
Designs generated by CRDAL show better performance.
Abstract
The engineering design research community has studied agentic AI systems that use Large Language Model (LLM) agents to automate the engineering design process. However, these systems are prone to some of the same pathologies that plague humans. Just as human designers, LLM design agents can fixate on existing paradigms and fail to explore alternatives when solving design challenges, potentially leading to suboptimal solutions. In this work, we propose (1) a novel Self-Regulation Loop (SRL), in which the Design Agent self-regulates and explicitly monitors its own metacognition, and (2) a novel Co-Regulation Design Agentic Loop (CRDAL), in which a Metacognitive Co-Regulation Agent assists the Design Agent in metacognition to mitigate design fixation, thereby improving system performance for engineering design tasks. In the battery pack design problem examined here, we found that the novel…
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