Dan Collins is a systems architect and practitioner focused on building reliable, controllable generative AI systems in real-world environments. His work centers on the design of agentic architectures, memory and state management, and governance mechanisms that mitigate uncertainty, hallucination, and unintended behavior in large language model–based systems. With experience spanning enterprise software, applied AI, and complex decision systems, he emphasizes practical engineering constraints over theoretical idealizations. This book reflects that perspective, translating architectural principles into implementable patterns for high-reliability AI systems.