CLIENT
Global Logistics Provider (Anonymized)
TIMELINE
10 weeks
SERVICES
AI Agents, Workflow Automation, System Integration
STACK
CrewAI, LangGraph, PostgreSQL, Apache Kafka, Docker
// 01
TheProblem
50,000+ shipments monthly. Manual routing decisions. Hours lost to spreadsheets and phone calls.
A global logistics provider managing 50,000+ shipments monthly was losing efficiency to manual routing decisions. Their operations team spent hours each day resolving exceptions — delayed containers, capacity mismatches, and regulatory holds — using spreadsheets and phone calls. Each exception required context from 4-5 different systems, and decisions were highly dependent on individual operator expertise.
The company had attempted two prior automation projects using rule-based systems, but the combinatorial complexity of logistics exceptions made hard-coded rules unmanageable. They needed a system that could reason about ambiguous situations the way experienced operators do, while maintaining full auditability for compliance.
// 02
OurApproach
Specialized AI agents that reason about ambiguous logistics exceptions like experienced operators.
We architected a multi-agent system where specialized AI agents handle different exception categories: routing optimization, capacity allocation, regulatory compliance, and customer communication. Each agent operates within a constrained decision space defined by business rules, but uses LLM reasoning to handle the ambiguous middle ground that rule-based systems cannot address.
The agent orchestration layer, built on LangGraph, manages agent coordination and implements a human-in-the-loop pattern for decisions above configurable confidence thresholds. All agent decisions are logged with full reasoning chains, satisfying the compliance team's auditability requirements.
We integrated the system with existing ERP and TMS platforms through Apache Kafka event streams, ensuring real-time data flow without requiring changes to upstream systems. The agents consume shipment events, cross-reference against live constraints, and either execute decisions autonomously or escalate with recommended actions and supporting evidence.
A critical design choice was implementing progressive autonomy: the system started with 100% human approval, gradually reducing oversight as confidence calibration improved. This built trust with the operations team and allowed us to identify edge cases safely.
// 03
TheResult
60% of routing exceptions handled autonomously with 97.3% accuracy.
After the progressive rollout, the agent system now handles 60% of routing exceptions autonomously with a 97.3% accuracy rate on those decisions. The remaining 40% are escalated with pre-analyzed context, reducing human decision time from 25 minutes to 4 minutes per exception.
Delivery schedule accuracy improved 2.4x because the agents can process constraint changes in real-time rather than waiting for batch review cycles. The operations team has shifted from reactive exception handling to proactive route optimization, leveraging insights surfaced by the agent analytics layer.
The system processes 3,200+ decisions daily and has maintained compliance audit standards with zero regulatory flags since deployment.
// IMPACT
reduction in manual routing decisions
to full production with rollback safety
improvement in delivery schedule accuracy
“The agent system handles edge cases we didn't even know we had. Our ops team went from firefighting to strategic planning in a matter of weeks.”