Morphogenetic Harness: What LLM Agents Should and Should Not Borrow from Developmental Biology

Developmental biology offers two kinds of inspiration for LLM agent design: structural principles (cell differentiation, morphogen gradients, gene regulatory networks) and dynamic processes (evolution, natural selection, population dynamics). We build a morphogenetic harness—a framework that applies both to LLM agents—and test which kind of inspiration actually helps. In Experiment 1 (static harness), we route queries to task-specific "cognitive skills" inspired by cell differentiation, testing on GSM8K, HumanEval, and MMLU across three LLMs. Skill routing significantly improves accuracy over unprompted baselines (Wilcoxon signed-rank $p<0.05$ for Haiku and Grok), with gains concentrated on math and logic. The gain over a generic chain-of-thought prompt is not significant in aggregate; a cross-routing control (applying the wrong skill) instead pinpoints correct task–skill matching as the operative mechanism (40pp drop on math under mis-routing). Ablation reveals that neither structure nor domain knowledge alone suffices—their synergy is key, mirroring the dual role of morphogens in biological patterning. In Experiment 2 (dynamic harness), we run evolutionary LLM colonies with bio-inspired reproduction, lateral inhibition, and fitness tracking. We find that evolution provides no benefit beyond the pass@k effect of running multiple agents. Cross-routing experiments confirm that the value of biological metaphor lies in the blueprint, not the process: static differentiation helps, but runtime evolution does not. We formalize this distinction through Ashby's Law of Requisite Variety and discuss implications for the emerging agent harness paradigm.