Agent Workflows
60% of Routing Decisions Automated with AI Agents
The Challenge
A logistics provider managing 50,000+ shipments monthly was losing efficiency to manual routing decisions. Two prior rule-based automation attempts failed due to combinatorial complexity.
Our Approach
We designed a multi-agent architecture on LangGraph with progressive autonomy — starting at 100% human approval and reducing oversight as confidence calibration improved.
The Execution
Delivered across 10 weeks with the following technology stack:
How we worked
Discovery
Deep-dive into existing systems, constraints, and stakeholder interviews.
Architecture
Design the system blueprint, data models, and integration points.
Prototype
Ship a working slice end-to-end to validate assumptions.
Build
Full development with weekly demos and continuous integration.
Deploy
Production rollout with monitoring, rollback plans, and training.
Scale
Performance tuning, documentation, and knowledge transfer.
The Results
- 60% of routing exceptions handled autonomously (97.3% accuracy)
- Human decision time reduced from 25 minutes to 4 minutes
- 3,200+ decisions processed daily with zero regulatory flags
Architecture Overview
The Future
This engagement established a foundation we continue to build on. The systems we shipped are now handling production workloads, and the architecture we designed is positioned for the next phase of scale.
The agent system handles edge cases we did not even know we had. Our ops team went from firefighting to strategic planning.