How is agentic AI different from traditional supply chain automation?

Traditional automation follows predefined rules: if X happens, do Y. It breaks down when situations don't match the rules. Agentic AI can reason through novel problems, query multiple systems to gather context, evaluate options, and decide on the best course of action. It handles the exceptions and edge cases that rule-based systems cannot.

What are common use cases for agentic AI in supply chain?

Common use cases include: exception handling (late shipments, inventory shortages), cross-system coordination (synchronizing WMS, TMS, and customer systems), autonomous order management, predictive inventory optimization, carrier selection and routing, and automated claims investigation. AI agents can handle complex workflows that span multiple systems and require contextual decision-making.

How do AI agents integrate with existing supply chain systems?

AI agents connect to existing systems through APIs and standard integrations. They don't replace your WMS, TMS, or ERP—they sit on top as an intelligence layer that can read data from all systems, understand the full context, and take coordinated action across platforms. Implementation typically takes weeks, not months.

Is agentic AI safe for autonomous decision-making in supply chain?

Yes, when implemented with proper guardrails. Agentic AI systems operate within defined boundaries—they can make decisions autonomously for routine situations while escalating edge cases to humans. You set the rules for what actions require approval and what can be handled independently. The goal is augmenting human capacity, not replacing human judgment for critical decisions.

Agentic AI for Supply Chain

From systems of record to systems of action

Q: What is agentic AI in supply chain?

Agentic AI in supply chain refers to autonomous AI systems that can reason across multiple systems (WMS, TMS, ERP), handle exceptions independently, and take action without waiting for human intervention. Unlike traditional automation that follows rigid rules, agentic AI can evaluate complex situations, weigh tradeoffs, and execute multi-step solutions.

Your supply chain runs on dozens of disconnected systems. Agentic AI agents reason across all of them, handle exceptions autonomously, and take action without waiting for human intervention.

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How AI Agents Work

WMS

TMS

ERP

CRM

AI Agent Reasons Across Systems

Reroute shipment

Alert customer

Update inventory

The Problem

Your systems don't talk to each other

A typical supply chain runs on dozens of disconnected systems. ERP for financials. WMS for warehouses. TMS for transportation. Demand planning. Inventory optimization. Carrier portals. Customer service platforms.

Between these systems sits a human. A planner reconciling forecasts across spreadsheets. A coordinator calling carriers when shipments run late. A service rep toggling between screens to answer a simple question.

This model is breaking. Supply chains are getting more complex. Customer expectations are rising. Labor is scarce and expensive. The humans holding everything together are overwhelmed.

20+

Systems in average enterprise supply chain

70%

Time spent on exception handling

4hrs

Average delay to resolve supply chain exceptions

How Agentic AI Works

AI agents that reason and act

Perceive

Agents connect to your existing systems through APIs. They see data across WMS, TMS, ERP, and other platforms in real-time. No manual integration required.

Reason

Unlike rule-based automation, agents understand context. They evaluate situations, weigh tradeoffs, and determine the best course of action for each unique scenario.

Act

Agents execute multi-step solutions across systems. They reroute shipments, update inventory, notify customers, and escalate to humans only when necessary.

Use Cases

Automate what matters most

Exception Handling

Late shipments, inventory shortages, carrier issues. Agents detect problems, evaluate options, and execute solutions before humans even know there's an issue.

Order Orchestration

Complex orders that span multiple warehouses, carriers, and delivery windows. Agents coordinate across systems to optimize fulfillment automatically.

Inventory Optimization

Dynamic rebalancing across locations based on demand signals, lead times, and capacity constraints. Agents make decisions humans don't have time to analyze.

Customer Communication

Proactive updates when issues arise. Agents draft personalized messages, offer alternatives, and resolve problems before customers need to call.

Traditional vs Agentic

Why rules-based automation isn't enough

Vessel arrives late at port

Sends alert. Human investigates which orders are affected and what to do.

Identifies affected orders, evaluates alternatives, reroutes urgent shipments, notifies customers.

Demand spike for low-stock item

Triggers reorder when threshold is crossed. Often too late.

Detects pattern early, checks alternate sources, balances inventory across locations, expedites if needed.

Customer asks for order status

Agent toggles between 4 systems to piece together answer.

Queries all systems, synthesizes complete status, responds in seconds with proactive updates.

Ready to deploy AI agents?

See how OneTrack's agentic AI platform transforms fragmented supply chain operations into autonomous systems of action.

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