Multi-Agent Based Automation of Perishable Goods Expiration-Based Pricing

This workflow automates the identification and proactive discounting of perishable items (e.g., dairy, produce, prepared foods) based on real-time shelf-life data from your Warehouse Management System (WMS). By applying a pre-configured discount curve—starting with shallow markdowns and escalating as the expiration date nears—it converts inventory that was destined for the dumpster into recovered revenue. The operational upside comes from selling through 80-95% of perishable stock before spoilage, directly improving gross margin and reducing write-off costs.

The system replaces the daily, store-level manual process of checking dates, calculating discounts, and printing/placing new price tags. An orchestration agent ingests shelf-life data, executes pricing logic, and communicates markdowns directly to in-store systems, including Electronic Shelf Label (ESL) networks or mobile devices for staff. This eliminates 15-25 hours of weekly labor per store dedicated to perishable markdowns, reallocating staff to customer service or stocking activities.

Beyond simple discounting, the workflow uses agents to analyze sales velocity post-markdown. If initial discounts don't move product quickly enough, secondary agents can trigger steeper, time-bound 'flash' markdowns or bundle promotions. This ensures maximum sell-through, which frees up valuable chilled/frozen display space for fresh, full-margin inventory faster. The result is higher inventory turnover rates and improved availability of high-demand items.

Every automated price change is logged with its rationale: remaining shelf life, applied discount rule, and reference to the originating WMS data. A governance agent enforces configurable business rules, such as minimum margin floors based on live cost data from the ERP, preventing any automated action from eroding profitability. This creates a defensible, audit-ready record for finance and compliance, turning an operational process into a controlled financial lever.

Implementation connects agents to core enterprise systems: a Data Ingestion Agent polls the WMS/ERP for batch codes and dates; a Pricing Logic Agent applies the discount curve using a framework like LangGraph; and an Execution Agent pushes updates via APIs to in-store ESL systems (e.g., SES-imagotag), mobile task managers, or the POS. The architecture includes exception-handling queues for items needing manual review (e.g., damaged goods) and can be rolled out sequentially by distribution center or store cluster.

Automated, consistent markdowns create a reliable 'reduced price' section that customers learn to trust, driving traffic and loyalty. By ensuring nearly expired items are always priced to sell, the store floor appears fresher, reducing the presence of visibly old stock. This protects brand perception for quality while strategically using perishables as a traffic driver, creating a secondary marketing benefit beyond pure cost recovery.

This agent establishes a real-time, bidirectional link with Warehouse Management Systems (WMS) and ERP (e.g., SAP, Oracle) to ingest batch-level expiration dates, lot numbers, and current warehouse locations. It normalizes disparate date formats, reconciles inbound delivery schedules against system records, and flags data-quality anomalies for human review before the pricing logic engages. This replaces manual spreadsheet tracking and ensures the pricing engine operates on a single source of truth.

The core pricing agent applies configurable, time-based discount curves (e.g., linear, exponential, stepped) as a product approaches its expiry. It factors in base cost from the ERP, current sales velocity, category-level waste targets, and even local store footfall forecasts. The logic is not static; it can be adjusted via a control plane for promotions or high-priority clearance events. This automates thousands of daily markdown calculations that were previously rule-of-thumb or calendar-based.

This execution agent transforms discount decisions into system-specific actions. For e-commerce and marketplaces (Shopify, Amazon), it makes real-time API calls to update digital shelf prices. For brick-and-mortar, it generates batch files or direct commands to Electronic Shelf Label (ESL) systems and in-store Point-of-Sale (POS) systems, ensuring price parity or strategic differentiation. It includes error handling and rollback logic to maintain system integrity during updates.

A pre-flight compliance agent that validates every proposed price change against business guardrails before execution. It enforces minimum margin floors (using live cost data from the ERP), checks against Minimum Advertised Price (MAP) policies, and ensures discounts do not violate contractual vendor agreements. Any violation triggers an alert and routes the decision to a human-in-the-loop queue for exception handling, creating a critical control layer for automated systems.

This monitoring agent completes the feedback loop by correlating final markdown prices with actual sales data and, crucially, spoilage logs from store-level waste scanning systems. It analyzes the effectiveness of discount curves, identifying products where pricing was too conservative (leading to waste) or too aggressive (leaving margin on the table). These insights are formatted and fed back to retune the discount orchestrator, creating a continuous learning cycle that directly reduces shrink.

The central nervous system built on a framework like LangGraph that coordinates the multi-agent workflow. It manages the stateful execution sequence: triggering the ingestion agent, passing data to the pricing orchestrator, routing through the governance gatekeeper, and dispatching to the execution layer. It provides a unified observability dashboard showing throughput, exception rates, and key business metrics (waste avoided, revenue recovered), making the entire automated operation transparent and manageable.