Answers questions about distribution center daily throughput, dock turn times, pick/pack accuracy %, and storage capacity utilization. Orchestrates two sub-agents: **Data Insights**, which queries BigQuery via the Conversational Analytics API and BigQuery's built-in forecasting/contribution/anomaly-detection tools, and **Market Context**, which answers external questions via Google Search grounding.
Duration5:45 (Normal Pacing)
Resolution1080p Full HD (1920×1080)
UI Scaling1.25x High-DPI Scaled Text
Model Runtimegemini-3.5-flash (Vertex AI)
📋 Multi-Turn Conversation Flow
Turn 1 (Data Insights / BigQuery):"What is our DC inbound throughput, dock-to-stock time, and storage capacity utilization across facilities?" — Synthesizes internal BigQuery conversational analytics query and computes KPI summary.
Turn 2 (Market Context / Google Search):"What are standard warehouse dock-to-stock and order-picking accuracy benchmarks?" — Grounds analysis against external retail benchmarks and industry context.
Turn 3 (Visual Artifact / Matplotlib):"Render a bar chart comparing daily inbound vs outbound unit throughput across distribution centers." — Generates and renders a custom chart visualization artifact inline.
Turn 4 (Executive Canvas Presentation):"Create a 4-slide executive presentation summarizing the Warehouse & DC Operations analysis and recommendations above." — Automatically creates a 4-slide deck and showcases each slide via the bottom thumbnail rail.