Grain Marketing and Storage Decision Support Automation

This workflow automates the critical, time-sensitive decisions that directly impact farm revenue after harvest. It eliminates the manual burden of constantly monitoring volatile futures markets, local elevator bids, and storage logistics. By modeling cash flow scenarios against real-time pricing data and physical constraints, the system generates specific sell, store, or forward-contract recommendations. This creates operational leverage, allowing farm managers to focus on execution rather than analysis, while systematically capturing basis improvements and reducing price risk exposure.

Implementation integrates with commodity data APIs (e.g., CME Group, DTN), farm management software (John Deere Operations Center, Granular), and on-site grain bin monitoring systems. The orchestrator, built with frameworks like LangGraph, runs scheduled agents to evaluate thresholds. Critical controls include configurable pricing triggers, mandatory human approval for large transactions, and a full audit trail of recommendations, market conditions, and final decisions for financial reconciliation and analysis.

This workflow automates a critical operational bottleneck: the daily manual analysis of futures prices, local basis, on-farm storage capacity, and cash flow needs to decide whether to sell at harvest or store for later pricing. The operational upside comes from capturing 5-15% better prices by acting on precise thresholds without constant market monitoring and from optimizing logistics to reduce commercial storage costs. Savings are realized through margin protection and reduced administrative labor, turning a reactive, gut-feel process into a systematic, data-driven operation.

Implementation integrates with farm management software (FMS) like John Deere Operations Center or Climate FieldView for harvest data, brokerage APIs for market feeds, and grain bin sensors for real-time capacity. The orchestrator, built on frameworks like LangGraph, manages agent communication, enforces business rules, and routes exceptions. Required controls include pre-set pricing thresholds, maximum position limits, and mandatory human review for large deviations. Observability is provided through a dashboard logging all agent reasoning, market conditions analyzed, and actions taken for audit and strategy refinement.

Phase 1 establishes the core data ingestion and modeling engine. We integrate APIs from commodity exchanges (CME, ICE), local basis data feeds, on-farm storage telemetry, and harvest records from farm management software like John Deere Operations Center or Climate FieldView. An orchestration layer, built with LangGraph, validates and fuses this data, running cash flow simulations under different price and storage duration scenarios. This initial build automates the manual data aggregation and spreadsheet modeling that currently consumes hours of daily analysis, creating a single source of truth for marketing decisions.

Phase 2 introduces autonomous alerting and controlled execution. The recommendation engine monitors for user-defined pricing thresholds or optimal storage economics. When triggered, it generates an alert with a prescribed action (e.g., 'Sell 50% of Bin 3 corn') routed to a human-in-the-loop dashboard for review and override. Upon approval, the system can execute via integrated broker APIs or create sales entries in the farm's financial ERP. Phase 3 adds advanced observability, tracking recommendation performance against actual market outcomes to continuously refine models. This phased approach de-risks implementation while locking in ROI from reduced missed opportunities and optimized storage logistics.

Rollout begins with a phased deployment to a single commodity or region, integrating with core systems like John Deere Operations Center for harvest data and DTN or CME Group APIs for futures and basis. A central orchestrator, built on LangGraph, manages the workflow state, ensuring each pricing alert or storage recommendation is traceable back to its source data and model logic. This controlled launch allows validation of data quality and exception handling before broader scale.

Governance is embedded via mandatory approval gates for any executed action, with all model inputs, logic, and user decisions logged to a time-series data warehouse like Snowflake for audit. Observability dashboards in Grafana track recommendation volume, approval rates, and realized P&L impact. This architecture ensures financial controllers maintain oversight while the system handles the repetitive analysis of basis shifts and storage economics, scaling to manage thousands of acres and contracts.