\begin{abstract}
The transition of Large Language Models (LLMs) from passive generators to autonomous agents
has introduced significant challenges in reliability, security, and state management. Current agentic
architectures are often constructed ad-hoc, prone to ``hallucination cascades,'' infinite loops, and
prompt injection attacks. This paper argues that many of these failure modes can be analyzed using
control motifs long studied in systems biology, provided the comparison is made at the level of typed
interfaces and coordination structure rather than literal biological mechanism.

We develop a typed interface correspondence, expressed in the language of polynomial functors and
wiring diagrams, between Gene Regulatory Networks (GRNs) and agentic software systems. On top of this abstraction, we derive a typed
syntax for agent composition by mapping biological
mechanisms---including Quorum Sensing for consensus, Chaperone Proteins for structural validation,
Innate and Adaptive Immunity for layered security, Mitochondrial Signaling for bioenergetic resource
governance, and Endosymbiosis for neuro-symbolic integration---to software design patterns. The framework
provides ``Epigenetic'' state management, a \textit{Provenance Functor} for
trust-gated security, \textit{Epiplexity} for detecting epistemic stagnation, a \textit{Metabolic
Coalgebra} for resource governance, wire-level \textit{Optics} for conditional routing, graph-based
\textit{Morphogen Diffusion} for spatially varying coordination, and composable \textit{Coalgebraic State
Machines} with observational equivalence criteria.

Beyond the structural layer, we introduce a progression from \textit{temporal epistemics} (bi-temporal memory with append-only correction semantics and belief-state reconstruction) through \textit{adaptive structure} (pattern libraries, runtime watchers with three-category signal taxonomy, and experience-driven assembly) to a \textit{cognitive architecture} (dual-process System A/B mode annotations, sleep consolidation with counterfactual replay, social learning with epistemic vigilance, curiosity-driven exploration) and finally \textit{developmental staging} (critical periods, capability gating, teacher-learner scaffolding). This six-layer progression---structure, memory, adaptation, cognition, development, integration---is grounded in Dupoux, LeCun, and Malik's autonomous learning framework and validated by Hao et al.'s empirical finding that intervention count serves as a convergence proxy. A reference implementation with 1,130 tests, 81 examples, interactive demonstrations, and cross-subsystem integration tests illustrates the framework's practical feasibility.
\end{abstract}
