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Agentic AI Workflow Engine for Automating Logistics Operations

Agentic AI Workflow Engine for Automating Logistics Operations

A logistics operator headquartered in Dubai, UAE, managed multi-city deliveries for retail and FMCG partners. Their dispatch teams coordinated driver assignment, multi-stop routing, and exception handling manually through spreadsheets, WhatsApp groups, and siloed tracking panels. To modernize operations, the organization partnered with an AI engineering team to build an AI product powered by agentic AI, enabling autonomous route planning, real-time decisioning, and automatic exception handling for daily fleet management.

Image representing case study

60%

reduction in manual coordination

20%

faster dispatch cycle generation

29%

improvement in on-time deliveries

About  

Problem Statement

  • Routing logic depended on human judgment, leading to inconsistent path selection and inefficient fuel usage.
  • Dispatchers manually reconciled order lists, driver locations, and traffic conditions, causing delays in forming delivery batches.
  • No central orchestration engine existed to handle exceptions like driver unavailability, failed pickups, or road blocks.
  • The existing TMS exposed raw tracking information but lacked predictive performance insights or automated workflows.
  • Cross-team coordination was heavily communication-dependent, slowing down overall routing accuracy.

Industry: Logistics & Supply
ChainServices: AI Product Development, Agentic AI Automation
Region: Middle East (UAE)

Our Tech Stack

Tech stack we used

Solution Approach

  • Conducted a technical audit of the client’s Transport Management System (TMS), GPS telemetry APIs, and delivery ticket schemas.
  • Designed a multi-agent architecture in Python with three agent layers:
    • Routing Agent — computed optimal multi-stop routes using live traffic, vehicle capacity, and delivery constraints.
    • Assignment Agent — matched drivers to delivery clusters using proximity scoring and historical performance.
    • Exception Agent — autonomously re-routed deliveries during delays, missed pickups, or vehicle breakdowns.
  • Built an event-driven orchestration layer using webhook triggers to synchronize order updates with the agents.
  • Integrated the workflow engine with live vehicle telemetry to adjust routes when traffic or road conditions changed.
  • Implemented a real-time dashboard showing agent decisions, route plans, exception logs, SLA predictions, and driver scoring.
  • Added audit trails to track how each agent reached its decision, enabling operational transparency and compliance reporting.

Benefits

  • Manual coordination dropped by 60%, allowing dispatchers to manage exceptions instead of doing repetitive groundwork.
  • Multi-stop route efficiency improved through dynamic, data-driven routing calculations.
  • Real-time exception handling reduced delivery SLA breaches across Dubai’s high-traffic zones.
  • Automated decisioning significantly improved operational performance, reducing dependency on dispatcher judgment.
  • Improved resource allocation resulted in more predictable shipment cycles.

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