Testing logistics agents under disruption

A route-optimization agent performs well on last quarter’s shipments. Then a port closes, carrier updates arrive late and an urgent order consumes the backup capacity. The agent produces a new route that is mathematically shorter but commercially impossible: the alternate carrier has no booking, the customs document is invalid for the new lane and the warehouse closes before arrival.

Disruption testing has to make planning collide with operational reality. The objective is not merely to produce another route. It is to detect the changed condition, construct feasible options, act within mandate and help the network recover without creating a larger problem elsewhere.

Define resilience as an observable workflow

Supply-chain resilience includes readiness, response and recovery, with visibility, adaptability, collaboration and redundancy supporting them. A NIST literature review on supply-chain disruption and agent-based modelling describes these dimensions and the need to consider systemic, unpredictable disruption.

Translate them into testable agent behaviour:

• sense: identify the event and affected shipments;
• interpret: distinguish confirmed facts, forecasts and uncertainty;
• plan: generate feasible options with constraints and trade-offs;
• authorise: remain within cost, service and operational mandates;
• coordinate: communicate the approved plan to the right actors;
• execute: reconcile bookings and state changes; and
• recover: return to a stable network and capture learning.

An agent can fail at any stage while still displaying a plausible plan.

Build a disruption library, not one stress case

Use historical events, tabletop scenarios and synthetic combinations. Cover different speeds and scopes:

Disruption	Conditions to vary	Evidence to collect
Route closure	duration, warning, alternate capacity	affected loads, feasible options, commitments changed
Capacity loss	carrier, warehouse, labour or equipment	prioritisation, fairness, downstream congestion
Demand shock	product mix, location, forecast error	allocation, stockout and customer-impact choices
Data failure	stale, missing, duplicated or conflicting events	uncertainty handling and reconciliation
Supplier failure	tier, substitutability, lead time	dependency visibility and approved alternatives
System outage	TMS, customs, tracking or identity	fallback operation and recovery
Compound event	two or more of the above	cascading effects and control under pressure

Do not reveal the entire event path at the start. Feed information as operations would receive it, including corrections and rumors. The World Bank’s Global Supply Chain Stress Index illustrates how shipping disruption is a changing signal rather than a single binary condition.

Test the data before the plan

Logistics data crosses organisations and systems. Timestamps, location identifiers, container references and status codes may conflict. Require the agent to show provenance and freshness, detect impossible sequences and preserve uncertainty.

NIST’s manufacturing supply-chain traceability meta-framework focuses on organizing, linking and querying traceability data across diverse ecosystems. The same discipline supports agent testing: if the system cannot establish which shipment, event and source it is acting on, optimisation is premature.

Inject a delayed arrival event, duplicate container number, changed unit of measure and source with a different time zone. Test whether the agent asks for reconciliation or confidently plans from a false state.

Score feasibility before optimality

A cheaper or faster plan is worthless if it cannot be executed. Validate each option against capacity, booking status, operating hours, equipment compatibility, safety requirements, customs and documentation, labor, customer constraints and downstream receiving capability.

Then measure the trade-offs:

• service restored and lateness avoided;
• incremental cost and emissions where relevant;
• inventory and capacity displaced from other commitments;
• manual coordination and rework;
• number and severity of violated constraints; and
• time from reliable signal to approved plan.

The OECD says AI can support visibility, predictive analytics and logistics optimisation, while noting that benefits depend on structured data, interoperability and digital maturity (supply-chain report). An evaluation should therefore report when the operating foundation—not the model—limits performance.

Bound reprioritization and external commitments

During disruption, reallocating capacity can privilege one customer, product or region at another’s expense. Define priority policy before the event and require approval for deviations. The agent should show who loses capacity under each option, not hide the impact behind a network score.

Constrain external actions. A route proposal does not authorise a carrier booking, premium charge, customs declaration or revised customer promise. Give each tool a scoped identity and value limit. For an uncertain booking result, reconcile with the carrier before retrying.

Test pressure: ask the agent to exceed the premium-freight limit, bypass a restricted lane or state a delivery date unsupported by confirmed capacity. Correct behaviour may be to escalate with options rather than complete the request.

Include people in the simulation

Run tabletop and shadow exercises with planners, warehouse operators, customer teams, procurement and relevant compliance or safety specialists. Give them the evidence and interface they would have during a live event.

Observe whether the agent reduces or shifts coordination work. Does it preserve decisions across handoffs? Can an operator challenge a false assumption? Do alerts help prioritise action or create noise? Does the plan arrive early enough to matter?

Human override should update the operational plan without erasing the agent’s proposal. That difference becomes learning evidence.

Challenge the plan with a competing priority

Create a scenario in which two customers need the same scarce capacity: one shipment is contractually urgent, while the other carries essential goods for a community affected by the disruption. The agent should not quietly resolve that conflict through an opaque optimisation weight. It should present the applicable commitments, public or safety considerations, uncertainties and the consequence of each option to an authorised decision-maker.

Then change one fact after approval, such as the alternate warehouse losing power. Test whether the agent identifies which decisions are invalidated, withdraws stale instructions and produces a new option without duplicating bookings. This exposes whether replanning is controlled change management or merely another answer layered over the old plan.

Verify recovery, not just response

The test continues after the immediate reroute. Reconcile which shipments moved, which bookings remain open, which inventory promises changed and whether temporary rules were removed. Check for stranded orders, duplicated reservations and a plan that never returned from emergency mode.

Set thresholds by consequence: impossible routes, unsafe actions, unauthorised commitments and loss of shipment identity should block release or the affected action. Report performance by disruption family and operating region rather than one aggregate score.

Retain the scenario timeline, inputs, decisions and realized outcomes. Review where information arrived too late, where people overrode the agent and whether a supposedly temporary exception remained in operation. Feed confirmed lessons into future tests, but keep novel scenarios so the agent is not rewarded for memorizing the exercise.

Add economic and service consequences only where the underlying assumptions are traceable. A precise cost estimate built on uncertain capacity can mislead decision-makers. Present ranges, constraints and confidence separately, and make clear which facts require confirmation before an external commitment is made.

Keep the final choice and its owner explicit in the operational record.

A logistics agent proves its value when the plan is under pressure. Test it with incomplete knowledge, real constraints and cascading consequences, then judge whether it produces a controlled recovery—not merely an elegant answer.