LLM Hallucinations in Logistics: Causes and Corrections

As the logistics industry embraces automation and artificial intelligence (AI), terms like “AI agents,” “LLMs,” and “workflow orchestration” are often used interchangeably.

This creates confusion, especially when expectations for AI far exceed current capabilities. One of the biggest misconceptions in this space is that Large Language Models (LLMs) like GPT-4 can act as reliable autonomous agents in high-stakes, real-time environments, like global supply chains. But the

truth is more nuanced.

In this article, we’ll explore a critical issue in applied AI: LLM hallucinations. We’ll explain what they are, how they impact logistics workflows, and why deterministic automation platforms like ours are still the most reliable approach for real-world operational integrity.

Flying ships. Miniature vessels. Trucks driving on water. Some LLM Hallucinations.

What Are LLM Hallucinations?

Hallucinations occur when a language model produces outputs that are plausible-sounding but factually incorrect or unsupported by its training data or external tools. In the context of logistics and supply chain, hallucinations can take many forms:

• Making up nonexistent SKUs or locations

• Misinterpreting shipment status updates

• Executing the wrong API calls to TMS or WMS platforms

• Misrouting a request to the wrong carrier or mode

These errors often arise because LLMs are statistical predictors, not factual databases or reasoning engines. Even when integrated with tool-use capabilities (e.g., calling APIs, triggering workflows), they may hallucinate the parameters or context for those tools, especially when data is ambiguous or incomplete.

Real-World Examples in Logistics

Let’s examine how hallucinations can introduce risk in typical supply chain use cases:

1. Incorrect Rate Shopping

An LLM tasked with finding the cheapest carrier for a given lane may hallucinate route availability, base rates, or delivery estimates if it misinterprets API output or lacks context.

2. Invalid Tool Calls

Some agentic AI frameworks allow models to choose actions dynamically. Without guardrails, the LLM might call createPickupRequest() when it should call getShipmentStatus(), leading to operational errors.

3. False Exception Alerts

Hallucinations can trigger false positives, such as flagging shipments as delayed when they are not,resulting in unnecessary escalations or customer communication errors.

Why Deterministic Automation Still Matters

Despite their potential, LLMs aren’t ready to autonomously manage logistics operations. That’s where workflow automation platforms like ours come in.

Unlike agentic AI, our platform enables you to build deterministic, rules-based workflows that trigger the right action every time based on clearly defined data conditions. Think Zapier, but for the complex world of 3PLs, shippers, carriers, and freight systems.

✅ No hallucinations. No surprises.

✅ APIs executed with predictable logic.

✅ Full audit trails and operational control.

You can still integrate LLMs as assistants within these workflows, e.g., for summarizing shipment notes or generating draft responses to clients, but the core orchestration stays secure and transparent.

Preventing and Recovering from Hallucinated Actions

If you're experimenting with LLMs in your supply chain operations, here are practical ways to limit damage:

1. Use Structured Tooling with Schema Validation

Ensure all tool calls from the LLM are wrapped in schema-validated functions. Reject or ignore calls that don’t conform to expected parameters.

2. Keep AI in the Loop, Not in Control

Treat the LLM as a co-pilot, not the pilot. Use it to augment human decisions or enrich workflows, not to drive mission-critical automation.

3. Add Logging and Guardrails

Track every AI-suggested action, flag anomalies, and create “human-in-the-loop” checkpoints for sensitive steps.

4. Integrate with a Workflow Automation Layer

Let a deterministic platform (like ours) handle execution and orchestration logic. This reduces the risk of “AI going rogue.”

The Bottom Line: Agentic AI vs Workflow Automation

Feature	Agentic AI	Workflow Automation (Our Platform)
Executes dynamic tool calls	✅ (but may hallucinate)	❌ (static, rules-based)
Deterministic outcomes	❌	✅
Works without APIs	✅ (via reasoning or summaries)	❌ (requires clear API integration)
Reliable for mission-critical ops	❌	✅
Extendable with AI	✅	✅

For most logistics teams, the best approach today is to blend the power of automation with the flexibility of AI—using each where it excels.

Ready to Automate Smarter?

If you're looking for a reliable automation platform purpose-built for the logistics and supply chain ecosystem, we’re here to help.

✨ Explore our integrations 

⚙️ Build custom workflows without code 

🔒 Get operational control and reliability from day one

Final Thoughts

The future of logistics will include AI—but not at the expense of operational accuracy. By understanding the limits of LLMs and designing around their strengths and weaknesses, we can build a smarter, safer, and more automated supply chain.

Stay curious, but stay grounded.