Finny: A Multi-Agent System for Structured Decision-Making with LLMs

Finny is a multi-agent system that demonstrates how large language models can perform structured decision-making by applying domain-specific rules to multiple related scenarios. Leveraging foundation models with Retrieval-Augmented Generation (RAG), the system applies Standard Operating Procedures (SOPs) for intelligent forecast refinement at scale. Finny employs a two-stage architecture: a knowledge base agent that retrieves and applies domain rules while analyzing historical patterns, and a conversational agent enabling interactive refinement. In user acceptance testing (UAT), the system achieved 97.6% alignment with expert judgment across 124 evaluations (31.5% complete, 66.1% partial), with quantitative validation showing 5.89% mean deviation and 0.993 correlation against human decisions across 1,280 data points. This production-deployed system reduces manual analysis time by 70%, translating to 2,400 annual hours savings in the piloted teams.