\section{Conclusion}

The transition from ``Prompt Engineering'' to ``Agentic Engineering'' requires moving beyond component-level
optimization toward principled architectural design. Current methodologies often lack the formal foundations
needed to guarantee system-level properties like termination and error suppression.

In this paper, we have demonstrated that Gene Regulatory Networks (GRNs) provide a proven architectural blueprint
for distributed, stochastic information processing. By formalizing this analogy through Applied Category Theory,
we have derived a suite of robust design patterns:
\begin{enumerate}[leftmargin=*]
\item \textbf{Robustness:} The use of Quorum Sensing and CFFL topologies to filter stochastic noise.
\item \textbf{Validity:} The use of Chaperone Proteins to enforce structural determinism on probabilistic outputs.
\item \textbf{Security:} The identification of Prion-like prompt injections and the topological defenses required
to stop them.
\item \textbf{Evolution:} The mapping of Horizontal Gene Transfer to dynamic tool loading and Endosymbiosis to
neuro-symbolic integration.
\end{enumerate}

We conclude that biomimetic topology offers a promising direction for reliable AI agents. The control structures
that emerged in biological systems---from metabolic constraints to symbiotic integration---address the same
fundamental challenges of distributed, stochastic information processing that agentic architectures face today.
