Manufacturers face new legal risks as agentic AI transforms supply chains

Agentic artificial intelligence is moving from theory into day-to-day supply chain work, and that shift is creating a new kind of legal exposure for manufacturers. Foley & Lardner says these systems are no longer limited to making recommendations for people to review; they are increasingly being used to place orders, alter stock levels, select carriers and reroute shipments with little or no human intervention.

That change matters because the benefits of machine-speed decis...

ion-making come with correspondingly fast-moving risks. Foley’s lawyers point to several failure modes that manufacturers should expect: too much inventory when an agent misreads demand signals; stockouts and production halts when safety stock is reduced on stale supplier data; avoidable air freight costs when a delay is wrongly inferred; and damage to temperature-sensitive goods when a shipment is diverted onto an unsuitable route.

The commercial appeal is clear. Automated systems can cut response times and reduce labour costs, while also helping businesses react to disruption without waiting for manual approval. But the same autonomy that makes these tools attractive also makes mistakes harder to catch and harder to undo. Venable, in a separate article on agentic AI governance, said the technology is spreading across internal databases, workflow tools and financial systems, underscoring the need for stronger compliance programmes around data handling, privacy and vendor management.

One of the biggest problems for manufacturers is that standard technology contracts often do not match the scale of the risk. Foley notes that AI vendor agreements frequently limit liability to fees paid, which in practice may mean little more than a subscription charge. They also commonly exclude consequential damages, even though the most likely losses from an autonomous supply chain error are exactly the kind of knock-on harms that exclusions are designed to capture, such as lost production, expedited freight and carrying costs.

That creates a difficult causation problem as well. When an autonomous agent makes a bad call, it may not be immediately clear whether the fault lies with the model, the underlying data, the configuration, the vendor, the system integrator or the manufacturer’s own oversight failures. In that sense, the technology can explain how a decision was made, but not who should bear the consequences.

Gartner has separately warned supply chain leaders about “agent washing”, the practice of marketing conventional software as if it were autonomous AI. Its advice is to concentrate on proven uses, strengthen data and governance foundations, and adopt agentic capabilities in a measured sequence rather than rushing to scale tools that may not yet be genuinely autonomous.

Foley argues that manufacturers should respond on two fronts: system design and contract design. On the operational side, companies should define clear limits on what an agent can do on its own, including thresholds for spend, volume and route changes that trigger human review. They should also build in override tools and kill-switches, backed by monitoring dashboards that allow teams to intervene before a flawed decision becomes expensive.

Data quality is another pressure point. Many failures start not with the model itself, but with bad inputs. Foley says contracts should spell out who is responsible for validating data, what standards apply and what happens if those standards are not met. That is especially important in supply chains where stale or duplicated information can quickly cascade into poor autonomous decisions.

The firm also recommends a tougher approach to liability allocation. If a system is allowed to act without prior approval, then the vendor’s contractual risk should reflect that reality through more realistic caps, carefully drafted indemnities, insurance requirements and narrower damage exclusions. Audit trails and decision logs are equally important, both for investigating disputes and for proving what the agent knew when it acted.

The broader message from lawyers and industry analysts is that agentic AI is no longer a distant possibility for supply chain teams. As adoption accelerates, the organisations best placed to benefit will be those that put guardrails in place before they hand real authority to software. Once an autonomous agent makes a costly mistake, the resulting dispute is likely to be judged by contract law and ordinary liability principles rather than by the logic of the model itself.

Source: Noah Wire Services