When Warehouses Start Thinking: LLM Agents as Operational Co-Pilots

Warehouse operations generate an enormous amount of structured data every day—orders, locations, volumes, workloads, shifts, and performance metrics. Traditionally, this data is handled by dashboards, reports, and optimization models that require expert interpretation. While these tools are powerful, their insights often remain locked behind technical interfaces.

This raises an important question:What if warehouse systems could explain themselves?

Recent progress in large language models enables a new type of interaction with operational systems: LLM Agents acting as operational co-pilots, capable of reasoning over data, coordinating with analytical models, and communicating insights in natural language.

A well-organized workspace that promotes efficiency.

Problem Overview: Balanced Work Assignment in Warehouses

The use case focuses on a common challenge in warehouse operations: fairly distributing picking work among operators.

Warehouse tasks are organized into tours, where each tour specifies:

• The quantity of products to be picked,

• The total volume (desi),

• The warehouse locations involved.

Operators complete a tour by collecting the items and delivering them to the packaging area before receiving the next assignment. This cycle continues until all tours are completed.

From an operational standpoint, the ideal outcome is for all operators to finish the day with similar workloads. In practice, this is difficult due to:

• Uneven product distributions,

• Differences in volume and quantity per tour,

• Varying distances between warehouse locations.
  

To address this complexity, an optimization-based job assignment model was previously developed to balance workload across operators by considering the number of tours, total quantity, and total volume.

Extending the Solution with LLM Agents

In this study, the optimization model is taken one step further by embedding it into an agent-based AI framework. The goal is to help warehouse managers interact directly with insights, rather than raw data or mathematical outputs.

Two LLM Agents are designed for this purpose, each serving a distinct operational role.

Agent 1: Warehouse Work Order Summary Agent

The first agent acts as an intelligent summarizer for warehouse operations. Instead of manually analyzing spreadsheets or system dashboards, managers receive a clear, human-readable overview of the day’s workload.

This LLM Agent analyzes all warehouse work orders and produces a concise operational summary that includes:

• The total number of tours scheduled,

• The total quantity of products to be picked,

• The total picking volume (desi),

• An estimation of the number of operators required to handle the workload efficiently.

By translating analytical outputs into natural language, this agent enables faster decision-making and improves communication between analytics teams and warehouse operations.

Why This Matters

This use case demonstrates how LLM Agents can complement traditional analytical models, rather than replace them. Optimization ensures mathematically sound decisions, while LLM Agents:

• Improve interpretability,

• Reduce cognitive load for decision-makers,

• Bridge the gap between technical solutions and operational execution.

As Agentic AI continues to mature, such hybrid approaches open the door to new applications where complex analytics become directly usable by non-technical business stakeholders.