Wildfires are increasing in frequency and intensity, yet many prevention and mitigation decisions still rely on static administrative boundaries or ad hoc clustering of incidents. Meanwhile, public wildfire data streams (e.g., satellite active fire detections) provide abundant point observations but are noisy and difficult to translate into actionable plans. During I-GUIDE's Summer School 2026, this team developed an AI-agent-centered planning system that uses an LLM as a “geospatial planner” to transform natural-language planning goals into auditable, executable spatial workflows and optimized mitigation districts. The wildfire case serves as a concrete, high-impact use case because active fire detections are naturally point-based and suitable for generating contiguous “planning districts” for staged resources and prevention investments.