Alchemist: Accelerating Decision Optimization with Specialized Autonomous Agents

This post introduces Alchemist, an agentic AI system for decision optimization. It details a modular, agent-based architecture composed of four specialized autonomous agents: Decision Process Extractor, Simulator Creator, Solver Recommender, and Optimization Formulator. The post explains how these agents collaborate to streamline the traditionally slow, handoff-heavy optimization workflow, replacing weeks of coordination with minutes of interactive iteration. It highlights the use of LLMs, persistent memory, and autonomous coding systems to enable business users to directly manage and iterate on optimization pipelines. A case study on optimizing nursing staff schedules demonstrates the practical application of these agents.