I designed this agent around the real work of reviewing delivery problems: I chose the signals, defined the evidence needed for a next step, and set the handoff rules for human review. It turns delayed or conflicting orders into a clear next step without taking consequential decisions away from people.
Order #2184 is surfaced with the context an operations person needs to investigate the shipment and update the customer.
What the agent prepares
An investigated queue with the evidence needed to act
The source export remains unchanged. The agent applies four approved checks, gathers the available order context, proposes the next safe step, and routes risk or uncertainty to a person.
200Orders checked
40Exceptions found
38Cases for review
2Low-risk closures
View the review queue in your browser
Prototype boundary: the dataset represents 200 Shopify-like synthetic orders. The prototype does not connect to carriers, send customer messages, issue refunds, or change an order. Those actions require approved integrations, policies, and human oversight.
01 / Context
Order teams lose time when the same export must be checked row by row
A late fulfillment, missing stock, missed delivery date, or refund-shipment mismatch can be easy to miss in a large export. The operational cost is delayed attention, inconsistent triage, and a manager who cannot quickly see the work queue.
Who is affected
Operations analysts need a prioritized queue. Managers need a clear view of volume and severity. Customers feel the consequence when an exception is noticed too late.
Why rules first
Fulfillment delay, inventory mismatch, delivery delay, and refund-versus-shipment conflict are factual comparisons, not subjective AI judgments. Each follows a deterministic rule that is easier to test, explain, and audit.
What stayed out of scope
Explaining why an issue happened, customer messaging, refunds, and inventory changes remained human decisions. The export can confirm a conflict, but not prove its operational cause.
02 / Solution
A clear investigation path from signal to a safe next step
The agent turns a signal into a bounded investigation. It checks the export, gathers the available evidence, proposes an approved next step, and hands over anything uncertain, sensitive, or financially consequential.
01Observe
Check the export
Keep source rows intact and confirm each signal has the fields it needs.
02Detect
Apply four checks
Look for fulfillment delay, inventory mismatch, delivery delay, and refund-versus-shipment conflict.
03Investigate
Gather the context
Assemble order status, tracking evidence, severity, and the reason the case was flagged.
04Propose
Recommend a next step
Prepare a follow-up, monitoring, or review recommendation from approved rules and evidence.
05Hand over
Keep people in charge
Any uncertain, sensitive, or high-impact case goes to a person before action.
03 / Production
The agent earns trust through safe handoffs and measurable investigation quality
This prototype does not claim a time or cost saving. A production pilot would compare a defined manual baseline with the workflow, then monitor quality as volumes and exceptions change.
Business measures
Time from exception to first review, open-queue age, exceptions processed per analyst, and avoidable customer contacts
Quality measures
Rule precision, false positives, missed exceptions, human override rate, and time to resolve an error
Governance
Named rule owners, access controls, run logs, alerts for failures, and a human fallback for every unsafe route
Measurement approach informed by the NIST AI RMF Measure guidance ↗, which recommends comparison with manual or human baselines and tracking error response.
04 / Evidence
What the prototype demonstrated before live integrations
Observed in the prototype
What it demonstrates
40 exceptions, 10 of each designed type
The rules ran consistently across the synthetic input
38 cases routed to review
The safety boundary held for all but two strict low-risk cases
6 recorded review decisions
The workbook captures decision, reason, reviewer label, and timestamp
