We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment. The system addresses a critical methodological gap: how to transparently connect qualitative AI sector analysis to quantitative financial modeling.
We present GovAI-Scout, an LLM-augmented autonomous agent that identifies, evaluates, and economically models high-impact AI deployment opportunities in government entities. The system combines a Claude-based reasoning layer for sector analysis and use case discovery with a structured econometric engine featuring government-realistic failure modes: procurement delays (6-24 months), cost overruns (45% probability per Standish CHAOS), political defunding risk (3-5% annual), and adoption ceilings (75-82%).
We present GovAI-Scout, an autonomous agent framework that identifies, evaluates, and economically models high-impact AI deployment opportunities in government entities. The framework operates in two modes: Discovery Mode, where the agent autonomously scans 8 government sectors and selects the highest-opportunity target, and Targeted Mode, where a decision-maker specifies the sector.