Projects
Topsource

Intelligent Logistics with AI Agents

Architected AI-powered logistics platform using MCP agents & Strands framework with intelligent job assignment algorithms that automate delivery coordination and optimize driver efficiency.

2 min read

Impact

100+ concurrent jobs, automated delivery coordination, dynamic route optimization

AI Agents
MCP
Strands
Logistics
FastAPI
Mapbox

Overview

Led the development of an intelligent logistics platform leveraging cutting-edge AI agent technology to revolutionize delivery coordination and driver management. The system uses MCP (Model Context Protocol) agents and the Strands framework to create autonomous, intelligent job assignment and route optimization.

Technical Architecture

AI Agents & Framework

  • Implemented MCP agents for autonomous decision-making in job assignments
  • Utilized Strands framework for agentic workflow architecture
  • Designed multi-agent system with intelligent scheduling automation
  • Built FastAPI backend for high-performance API endpoints

Real-Time Intelligence

  • Integrated Mapbox APIs for real-time traffic analysis
  • Dynamic route optimization based on live traffic data
  • Proactive prediction of delivery delays and bottlenecks
  • Real-time driver efficiency tracking and optimization

Key Features

  • Intelligent Job Assignment: AI-powered algorithms match jobs to optimal drivers based on location, capacity, and availability
  • Automated Coordination: Autonomous agents handle delivery scheduling, reducing manual intervention
  • Dynamic Routing: Real-time route adjustments based on traffic conditions and new job priorities
  • Scalable Architecture: System handles 100+ concurrent jobs with consistent performance

Technical Challenges & Solutions

Challenge: Real-time Decision Making

Needed to process multiple concurrent jobs and make instant assignment decisions while considering numerous variables (driver location, traffic, capacity, priorities).

Solution: Implemented agentic workflow architecture where specialized agents handle different aspects of decision-making in parallel, with a coordinator agent making final assignments based on aggregated intelligence.

Challenge: Scale and Performance

Required system to handle growing job volume without degradation.

Solution: Built distributed agent architecture with FastAPI for high-throughput request handling, implementing efficient job queuing and priority-based processing.

Impact

  • 100+ concurrent jobs handled simultaneously
  • Automated delivery coordination reducing manual coordination time
  • Dynamic route optimization improving delivery efficiency
  • Proactive issue prediction reducing delays

Technologies Used

  • AI Frameworks: MCP (Model Context Protocol), Strands
  • Backend: FastAPI, Python
  • APIs: Mapbox APIs (routing, traffic, geocoding)
  • Architecture: Multi-agent systems, microservices
  • Team: Led 5-person development team

Role & Leadership

As technical lead, I:

  • Architected the overall system design and agent coordination framework
  • Led a 5-person development team through the entire development lifecycle
  • Designed the intelligent job assignment algorithms
  • Integrated external APIs for real-time data
  • Established scalability patterns for handling growing job volumes