Multi-Agent Based Automation of Grocery & CPG Perishable Goods Pricing

This workflow automates the high-frequency, data-intensive task of adjusting prices for perishable inventory based on real-time shelf life, delivery schedules, and in-store waste targets. It eliminates manual markdown processes and guesswork, directly linking expiration countdowns to margin-optimized discount curves. The operational upside comes from increased sell-through of at-risk stock, reduced spoilage costs, and labor savings from automated label updates and reporting, turning a traditional cost center into a profit-protection lever.

Implementation integrates with warehouse management systems for date tracking, IoT shelf sensors for real-time stock levels, and Electronic Shelf Label systems for in-store execution. The multi-agent architecture, built on frameworks like LangGraph, separates concerns for analysis, modeling, and governance, ensuring each price change is justified by shelf-life decay and current demand. Critical controls include pre-flight margin checks, anomaly detection to flag erroneous discounts, and configurable human review gates for high-value or outlier decisions, ensuring the system operates within strict financial guardrails.

This workflow automates the high-frequency repricing of perishable CPG and grocery items by directly linking price to real-time remaining shelf life and in-store conditions. It eliminates the operational bottleneck of manual markdown processes and markdown committees, converting perishability from a pure cost center into a dynamic pricing variable. The savings come from reduced waste, improved sell-through velocity, and labor leverage, directly protecting margin on high-risk inventory. Implementation requires integration with WMS for expiration dates, IoT shelf sensors for condition monitoring, and ESL or POS systems for price execution.

Implementation centers on a LangGraph-based orchestrator that sequences specialized agents for data fusion, discount curve calculation, and governance. The discount logic agent applies configurable curves—steeper as expiration nears—factoring in sensor data for quality deviations. A pre-flight agent enforces margin floors and brand price guards before execution. Rollout requires phased integration with systems like SAP ERP or Blue Yonder WMS, starting with a single category. Controls include daily waste reconciliation, anomaly alerts for abnormal discount rates, and manager dashboards for override and performance tracking.

Phase 1 establishes the data foundation, integrating IoT shelf sensors and warehouse management systems (e.g., SAP EWM, Blue Yonder) to create a real-time feed of product-level remaining shelf life and store-level waste metrics. This phase delivers immediate ROI by identifying at-risk inventory and enabling manual markdown targeting, reducing spoilage by 15-25%. Concurrently, we build the core orchestration layer using LangGraph to manage agent handoffs and enforce business rules.

Phase 2 operationalizes the autonomous pricing loop. The Discount Logic Agent, governed by configurable decay curves and store-level waste targets, calculates and proposes price changes. A pre-flight check agent validates each proposal against live cost data from the ERP and minimum margin rules. Approved changes execute via APIs to Electronic Shelf Label (ESL) systems and the POS. Phase 3 introduces closed-loop learning, where a monitoring agent analyzes sales velocity post-markdown to refine discount curves, creating a self-optimizing system that continuously improves sell-through and margin recovery.

A governance layer is non-negotiable for automated perishable pricing. It enforces margin floors, validates discount logic against waste targets, and routes anomalous decisions—like a price drop exceeding a configurable threshold—to human review queues in tools like ServiceNow or Jira. This pre-execution check integrates with ERP systems like SAP for live cost data and IoT platforms for real-time shelf-life signals, ensuring every automated decision is profitable and traceable before hitting the shelf or e-commerce API.

Rollout should be phased, starting with a single category in a controlled test environment using a digital twin of inventory and sales data. Phase two expands to a pilot region, with A/B testing against legacy pricing to validate margin and waste reduction. The final phase is global deployment, managed by feature flags in the orchestration layer (e.g., LangGraph) to allow instant rollback. Continuous monitoring via dashboards tracking key metrics—spoilage rate, margin impact, exception volume—is essential for iterative tuning and proving ROI.