Delhivery’s TransportOne has launched an agentic AI workforce comprising six specialised AI agents designed to support transportation and logistics operations. The system is built to work alongside human teams, handling repetitive coordination, analysis and decision-making tasks across procurement, planning, execution and customer operations.
 

The launch represents a shift from using AI as a general-purpose assistant to deploying multiple specialised agents, each designed around a specific logistics function.

Six AI agents for different logistics jobs

According to Delhivery, the TransportOne workforce includes six named AI agents:

• Vihaan: Planner

• Kabir: Procurement & Negotiation Manager

• Nayan: Logistics Coordinator

• Saanvi: Freight Auditor

• Anaya: Customer Success Manager

• Ved: Data Analyst
   

Three of the agents are voice-based and can conduct conversations over the phone, allowing them to interact with carriers and other stakeholders rather than simply generating text-based responses.

💰 AI moves into freight procurement

One of the more practical applications is freight procurement.

The AI workforce can support activities such as spot-rate negotiations, carrier coordination and procurement decisions. Instead of an employee contacting multiple carriers to compare rates and availability, an agent can handle parts of that interaction and bring the information back into the transportation workflow.

This is particularly relevant in transportation, where rates, capacity and availability can change quickly.

📑 Automated freight auditing

Another agent focuses on freight auditing.

The system can examine invoices and identify potential overcharges or discrepancies. This moves AI into a traditionally manual back-office process where teams often compare contracted rates, shipment details and invoices to identify billing errors.

For logistics operators handling large shipment volumes, even small discrepancies can become significant when multiplied across thousands of transactions.

🚚 Planning, coordination and service monitoring

Other agents are designed to support shipment planning and day-to-day coordination.

They can assist with activities such as monitoring transportation execution, coordinating with stakeholders and identifying potential service-level deviations. The objective is to reduce the amount of repetitive follow-up that operations teams need to perform manually.

Delhivery has already been deploying AI agents across internal operations and customer-support workflows. Its earlier disclosures describe agents interpreting unstructured information from customer queries, delivery-associate calls and failed deliveries before recommending or triggering operational actions.

🧠 From chatbot to digital workforce

The important distinction is the agentic approach.

A traditional chatbot generally waits for a question and provides an answer.

An AI agent can be designed to:

Monitor → Analyse → Decide → Take action → Escalate when required

That makes the technology much closer to a digital operations worker than a conventional chatbot.

Delhivery's broader technology strategy supports this direction. The company says it has built an agentic AI framework that allows AI agents to be deployed across its technology stack, using more than 15 years of proprietary shipment and telemetry data.

📊 Why this matters for logistics

Transportation operations involve thousands of small decisions every day:

Which carrier should be selected?
Is this rate reasonable?
Is the invoice correct?
Will the shipment miss its SLA?
Does the carrier need a follow-up?
What should happen if a truck cancels?

Historically, many of these decisions required human teams to monitor systems, make calls, send messages and reconcile information.

Agentic AI is beginning to automate parts of that workflow.

Delhivery also reported that it introduced an AI-agent-powered autonomous transport management system covering freight procurement, shipment planning, execution and invoice reconciliation.

The bigger trend

The significance of TransportOne's launch goes beyond six individual AI agents.

It points toward a broader change in logistics software:

One general AI assistant → Multiple specialised AI agents → Coordinated digital workforce

Instead of asking one AI system to understand every logistics task, companies can deploy specialised agents trained around specific workflows and connect them to the same transportation data and systems.

The next generation of logistics automation may not be one AI that does everything. It could be a team of specialised AI agents, each responsible for one part of the transportation operation.