Agentic AI in Action — Part 14 -Building a Store Performance Agent: Using LLMs and Maps to Identify

2026-03-17 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

A Store Performance Agent is demonstrated, combining structured data analysis, LLM reasoning, and spatial visualization to identify underperforming retail locations and generate actionable insights. The agent analyzes operational data, such as revenue, targets, foot traffic, marketing spend, staffing, competitor density, and customer ratings, for stores across US cities like New York, Los Angeles, and Chicago. It classifies stores performing below 80% of their target revenue as underperforming. An OpenAI client-based LLM then interprets these metrics to explain potential causes for poor performance and recommend specific actions. Finally, PyDeck visualizes these underperforming stores on a map, allowing decision-makers to prioritize interventions geographically. This agentic pattern aims to automate problem detection and insight generation, moving beyond traditional human-interpreted dashboards.

Key takeaway

For retail operations managers or data scientists tasked with optimizing multi-location performance, this agentic approach offers a powerful alternative to manual dashboard analysis. You should consider implementing a similar system to automatically detect underperforming stores, receive LLM-generated explanations for performance gaps, and visualize problem areas on a map. This can significantly reduce the time to identify issues and enable faster, more informed operational decisions, improving resource allocation and intervention effectiveness.

Key insights

Agentic AI combines data analysis, LLM reasoning, and spatial visualization to automate identifying and explaining underperforming business locations.

Principles

• Automate problem detection with agents.
• Combine structured data with LLM reasoning.
• Visualize insights spatially for clarity.

Method

The agent analyzes store performance data using Python, identifies underperforming locations, uses an LLM to generate explanations and recommendations, and visualizes results on a map.

In practice

• Use Pandas for data manipulation.
• Integrate OpenAI client for LLM reasoning.
• Employ PyDeck for map visualization.

Topics

• AI Agents
• Large Language Models
• Spatial Visualization
• Store Performance Analytics
• Structured Data Analysis

Code references

• Krishsriniv/store-performance-agent-using-llm-and-maps

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.