This talk will introduce the development of artificial Agents to model biological phenomena in molecular biology, biotechnology, and synthetic biology incorporating reinforcement learning, differential equation modeling of molecular dynamics, and agentic bio-causal reasoning. Agent to agent interaction with the A2A and PoR protocols, and MCP and API interfaces to Machine Learning (Neural Network) Models including causal reasoning models and bio-specific models will be discussed. Synthetic biology deals with huge possibility spaces in terms of the combinatorics of nuceotide and proteomic sequences in proposed novel genes and proteins and how to constrain possibility spaces into computable functional novel genes, genetic circuits, gene regulatory networks and novel functional proteins will be discussed. Hence the sheer complexity of biological phenomena requires advanced Agentic AI and machine learning models to efficiently process, find patterns in, and reason about these complex systems with hundreds of thousands of variables, millions of connections, and potentially trillions of parameters. The current state of Agentic Bio research will be covered and where the research needs to go will be elucidated. Finally an application of Agentic Inter and Intra-cellular Signaling will be presented in detail to see the nuts and bolts of how Agentic AI can model a biological phenomenon with molecular biological, medical, and synthetic biological applications. The presenter’s background includes advanced degrees in computer science and computational molecular biology with experience in bio-computational modeling including a computational neuroscience project at Stanford where the neurogenetic and synaptic development of the C.elegans’ brain was modeled. Synthetic Biology: the possibility spaces are endless!