Making supply chains resilient with agentic Interoperability

Interoperability is the missing rung in the ladder to achieve truly resilient supply chains. It’s the autonomy that supply chains have yearned for decades. Agentic workflows finally give us the tools to make it stick. Rather than just shuttling data, Agentic AI can convert clean data into decisions with guardrails, so when humans are unavailable, operations continue to move forward.

Why now?

Two regulatory and market currents make this urgent. First, standards like GS1 EPCIS 2.0 enable partners to describe “what, when, where, why, and how” events, which are table stakes for cross-company visibility. Second, policymakers have raised the bar on traceability (FDA DSCSA in the US) and transparency (EU Digital Product Passports under ESPR), pushing firms toward interoperable systems instead of one-off interfaces. Add shifting tariff regimes, and you have volatility on tap—the latest being reciprocal tariff resets and changes under Section 301. So brittle integrations become liabilities.

What agentic Interoperability looks like?

Classic integration moves messages. Agentic Interoperability makes decisions. Agents plan a task, call the right API, EDI, or data-space connector, validate against your canonical model, recover from errors, and escalate to a human only when needed. Think belt-and-suspenders autonomy: measure twice, cut once, and then execute fast. Off-the-shelf frameworks, such as LangGraph and Microsoft’s AutoGen, make it easier to compose these agents and keep humans in the loop for edge cases.

An Agentic scenario in practice is being formulated at the German Automaker, BMW.

BMW is a founding supporter of Catena-X, the automotive data space designed for sovereign, standardized data sharing across tiers. Here’s a pragmatic agentic pattern, representative of what this vision enables within hours, not weeks:

A vehicle health agent detects a cluster of seat-heating malfunctions in the field and raises a quality trigger tied to the affected batch.

A production agent at the plant acknowledges, quarantines work-in-process with the same batch, and auto-generates a rework order and replacement part pull.

A fulfillment agent updates shipments and notifies distribution centers to re-slot inventory, avoiding customer-facing promises you can’t keep.

A supply-planning agent readjusts short-term demand and safety stock, rolls the impact into a constrained forecast, and nudges procurement.

A partner agent posts an EPCIS event set into the data space so suppliers and service centers see the same ground truth. Humans still steer the ship, signing off on exceptions and safety-critical actions, but agents shorten the loop to hours. The point isn’t a chatbot on wheels; it’s dependable, standards-first coordination under stress.

Consider the economics. Warranty campaigns can bleed millions, and a poorly handled recall can sink a brand’s reputation for years. In BMW’s scenario, an agentic interoperability layer not only shortens the fault-detection loop but also saves downstream costs in service, inventory, and goodwill. The same is true for data: a missing supplier ID or unit-conversion error may seem trivial, but when multiplied across thousands of SKUs and partners, it becomes a significant tax on resilience. Interoperability, in practice, is the shield that lets firms absorb tariffs today and whatever disruptions come tomorrow.

Start by building your data supply chain

Interoperability rises or falls on data quality. Build a data supply chain where trustworthy data can travel in all directions and every stakeholder can act on the same insights. In practice:

Canonicalize events and IDs. Start with EPCIS 2.0 and GS1 identifiers. Don’t boil the ocean; instead, pick one flow (e.g., supplier ASN to receiving) and make it bulletproof. Create a thin interoperability layer. Expose ERP/WMS/TMS via versioned APIs. Use adapters to translate partners’ dialects into your canonical model. Instrument for drift and exceptions. Track straight-through rate, exception aging, partner-onboarding time, and schema-change lead time.

Govern the agents. Apply the NIST AI Risk Management Framework to ensure autonomy remains on a short leash where it matters most.

Pitfalls to dodge

Beware green-field fantasies. Wrap what you have and retire point-to-point interfaces gradually. Don’t let agents free-wheel; uncontrolled autonomy creates novel failure modes. Avoid bespoke semantics: if two partners mean different things by “lot,” no clever code will save you. And keep documentation public for partners; if you make it easy, they’ll integrate faster and more cost-effectively.

What to do next?

Choose one painful, cross-company process (chargebacks from ASN errors, recall response time, or Digital Product Passport attribute gaps).

Draw the golden path as a sequence diagram. Specify events, APIs, schemas, and decision checkpoints.

Stand up a pilot agent graph: validator → mapper → poster → exception desk. Add a deterministic fallback at every step.

Publish a sandbox and test data for partners. Visibility builds trust; trust speeds onboarding.

Do this, and you’ll build a wall shield against continuous disruption. Today it’s tariffs, it would be something else tomorrow, while competitors keep chasing their tails.

In the end, we spent two decades wiring ESBs and EDI with standards like EPCIS 2.0, data spaces like Catena-X, and regulatory tailwinds from ESPR and DSCSA, while also maturing agent frameworks. Now, Interoperability can become your new design choice. However, walk before you run, keep humans in the loop, and the ROI will follow.