Metabolic processes are messy. In biology, organelles like Mitochondria and Lysosomes don't follow a central "script"; they respond to chemical signals and negotiate resources. When building Cyto Agent , I wanted to mirror this decentralized intelligence using modern LLM agent patterns. In this post, we’ll dive into how to build a real-time cellular simulation powered by a "LangGraph-style" tool-calling orchestrator. The Problem: Scripted vs. Dynamic Intelligence Most simulations use hard-coded if/else ladders. if (pathogen) { defend(); } While efficient, it lacks the nuance of biological adaptation. Cyto Agent replaces these ladders with a Nucleus Agent —an LLM-powered orchestrator that perceives the cell state as unstructured data and decides on actions by reasoning through available tools. High-Level Architecture The system is split into three main components: The Engine (Simulation.ts) : A reactive state machine that handles the "physics" of the cell (ATP decay, glucose consumption, pathogen damage).…