Freight AI agent failure library
Audit the whole workflow, not only the model. A freight AI agent can fail while reading a document, matching a shipment, applying a rule, writing to the TMS, escalating an exception, or reporting its own result. Each failure needs evidence, a human gate, and a metric.
What counts as a freight AI agent failure
A failure is not limited to a wrong model answer. The model may extract the right date while the integration writes it to the wrong shipment. The agent may complete the action but fail to alert a person when confidence is low. A results report may also claim time savings that nobody measured.
The table below separates common failures by the evidence and control needed to find them.
| Failure | Evidence to inspect | Human gate | Metric |
|---|---|---|---|
| Wrong shipment match | Message, shipment identifiers, match rule, selected record | Review when identifiers conflict | Wrong-record actions |
| Missing attachment or page | Original message, attachment count, page count, extraction log | Stop when a required file is absent | Incomplete inputs caught |
| Stale rate or rule | Rate source, effective date, rule version, quote output | Approve prices and expired sources | Stale-source attempts |
| Unsupported inference | Source text, extracted field, confidence, model output | Review fields with weak evidence | Unsupported fields per 100 items |
| Duplicate action | Case identifier, prior actions, retry log, system response | Block repeated sends or writes | Duplicate actions prevented |
| Integration write failure | Request, response code, retry result, final TMS state | Escalate after the retry limit | Unresolved write failures |
| Weak escalation | Confidence, exception rule, timer, assigned owner | Require a named owner and deadline | Exceptions past deadline |
| Model or template regression | Version change, test set, before and after results | Hold release when tests worsen | Accuracy by version |
| Measurement error | Baseline, event logs, sampling method, report formula | Review claims before reporting | Reported versus verified result |
Evidence every production workflow should retain
Keep the original input, extracted fields, the rule or model version, the action sent to another system, that system's response, any human approval or correction, and a timestamp. Use one case identifier across the logs so a reviewer can reconstruct the event without guessing.
This matters in freight because data moves between several parties and is often re-entered. A UNECE paper on supply chain data quality describes how repeated re-entry can leave data open to interpretation and make timing and accuracy harder to maintain.
Set human gates by consequence
An agent can handle a low-risk, reversible action when the input is complete and the rule is clear. A person should review actions involving price, payment, a customer promise, a dispute, a compliance decision, or an unusual exception. The gate should be written as a rule, not left to the agent's judgment.
If the agent cannot detect or correct an error, it needs a reliable way to stop and ask for help. NIST's AI Risk Management Framework calls for documented human oversight, testing before deployment, regular testing in operation, and production monitoring.
How often to audit a freight AI agent
Set the review frequency according to volume and potential harm. Review a new workflow and every major change more closely. Keep automated alerts running between formal reviews. A stable system still needs sampling because document formats, counterparties, integrations, and operating rules change.
Track results by failure type and workflow version. A single overall accuracy number can hide a serious problem in one carrier, document type, or action.
A useful incident record
For each incident, record the date, workflow, input, expected result, actual result, impact, root cause, correction, and the test used before release. This turns a failure into a check that can run again.
Questions to ask a vendor
- Show the failure register for a production workflow.
- Show one event from source input through the final system response.
- Explain which actions stop for human approval and why.
- Show what happens when an integration times out or returns an error.
- Explain how a changed model, rule, or template is tested before release.
- Name who responds to an incident and what the customer receives afterward.
Sources and method
- NIST AI RMF Core, including human oversight, testing, monitoring, incident response, and change management guidance.
- UNECE White Paper on Data Pipeline Concept for Improving Data Quality in the Supply Chain.
This library applies those controls to common freight workflows. It is an audit framework, not a record of industry failure rates.
Frequently asked questions
A freight AI agent failure is any case where the system reads, decides, acts, escalates, or reports incorrectly. The failure may come from the input, model, rule, integration, or monitoring layer.
Set the review frequency according to volume and potential harm. New or changed workflows need tighter review. Production monitoring should continue between formal audits.
Only for low-risk actions that are contained and reversible. Keep pricing, customer commitments, disputes, payments, and unusual exceptions behind human approval.
Store the source input, extracted fields, confidence or rule used, tool action, system response, human approval or correction, timestamp, and a shared case identifier.
Review one live workflow
Map its failure points, approval gates, evidence, and production checks.