Build a Simulation-First AI Agent to Monitor Supply Chain Delivery Failures
The objective is not to connect an AI agent to a live TMS as quickly as possible. It is to establish whether the agent can diagnose delivery failures reproducibly before it receives production access.
1. Model the delivery chain and decision points
Represent the operational flow across warehouse preparation, transport legs and last-mile delivery. Identify the timestamps, cut-offs, hand-offs and service commitments that determine whether a shipment is on time.
2. Inject controlled failure scenarios
Use a simulation or digital-twin environment to create known late-delivery cases. Vary where the delay originates so the agent must distinguish an upstream cause from a downstream symptom.
3. Define agent scope and ownership
Assign each agent a bounded operational responsibility. Define which exceptions it may investigate, which evidence it may access and which team owns the resulting action. Keep production-changing actions out of scope during validation.
4. Expose narrow, read-only analytical tools
Give agents access only to the shipment events and calculations needed to investigate exceptions. Prefer explicit tools for retrieving events, comparing timestamps and calculating delays over unrestricted system access.
5. Require evidence for every diagnosis
Every alert should identify the affected shipment, the observed deviation, the evidence used, the inferred root cause and the responsible operational scope. Retain tool calls and metrics so a planner or operator can reconstruct the reasoning.
6. Validate in shadow mode
Run the agents against controlled scenarios and then, where appropriate, against read-only production data without allowing autonomous execution. Compare diagnoses with known causes or operator conclusions. Track false positives, missed exceptions and incorrect ownership assignments.
Acceptance gate
Do not move from simulation to production merely because the agent produces plausible explanations. Require repeatable diagnosis, traceable evidence, stable ownership rules and an explicit escalation path for ambiguous cases. Production access should expand only when those controls hold under realistic operating conditions.