Jaiden Howard

Behavioral economics provides a framework for understanding how humans make decisions in real-world conditions—accounting for context, perception, and cognitive limitations. From pricing and purchasing to resource allocation, our economic systems have long been designed around how humans interpret information and respond to uncertainty. Today, that foundation is beginning to shift. As AI evolves from passive tools to autonomous agents, we are entering a new paradigm: the Agentic Economy—a system where decisions such as negotiation, procurement, scheduling, and market execution are increasingly handled by data-driven, autonomous systems. This session explores the transition from Behavioral Economics, which models human decision-making, to Algorithmic Utility, where decisions are continuously optimized based on real-time data. In this model, humans remain the Architects of Intent, defining goals and constraints, while agents become the Engine of Execution. As a result, the economy becomes increasingly De-Rendered, shifting away from interfaces designed for human interaction toward systems optimized for machine-to-machine communication. We will examine key technical signals already pointing in this direction, including the rise of LLM-powered agents with tool use and memory, multi-agent systems coordinating across APIs, and emerging standards like Model Context Protocol (MCP) that enable structured context sharing between systems. We will also explore real-time data pipelines and optimization systems used across pricing, logistics, and financial systems to reduce inefficiencies in decision-making. For engineers, this shift introduces new design priorities: building for non-human users, enabling safe autonomous execution, and designing systems where trust, constraints, and outcomes are embedded by design. The era of human-driven decision systems is beginning to fade. The era of the Agentic Economy is emerging.