Agentic AI (Logistics)

Agentic AI in logistics refers to artificial intelligence that doesn't just recommend actions but takes them. Where traditional AI might suggest the best carrier for a lane or flag a billing discrepancy for review, agentic AI can autonomously execute a chain of decisions – building a load, querying carriers, tendering to the optimal option, generating documents, and sending notifications – intervening only when something falls outside its confidence threshold or predefined rules.

The "agentic" distinction is about autonomy and multi-step reasoning. A standard AI model might score carriers or predict ETAs. An agentic system can receive an order, determine the optimal shipment configuration, compare rates, select a carrier, tender the load, generate the BOL, send the ASN, and monitor the shipment – handling the entire workflow as a connected sequence rather than isolated tasks. When an exception occurs – a carrier rejects a tender, a shipment runs late, an invoice doesn't match – the agent can follow escalation logic, re-tender, alert stakeholders, or flag for human review depending on the severity.

This represents the next evolution in logistics automation. First came digitization (moving from paper to screens). Then came rule-based automation (if X, then Y). Then machine learning (pattern recognition and prediction). Agentic AI adds the ability to reason through complex, multi-step workflows and take action – compressing what used to require a coordinator's full attention into something that runs in the background. The human role shifts from executing tasks to setting policies, reviewing edge cases, and focusing on the strategic work that actually requires judgment.