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The RCA time sink
When a production issue occurs (a shipment ETA stops updating, a carrier integration drops data, an appointment scheduling workflow fails), the first step is root cause analysis. An engineer must trace the code flow from the symptom back to the source: which service processed the data, what transformation was applied, where the failure occurred, and why. In a microservices architecture with dozens of services, this trace can take hours. During a P0 incident affecting customer operations, those hours are costly.
AI-assisted tracing
FourKites engineers use AI to accelerate root cause analysis. The AI reads the codebase, understands the service architecture, and traces execution paths based on natural language descriptions of the symptom. An engineer can type 'Trace the code flow from GPS location ingestion to milestone detection' and receive a detailed walkthrough of the services, transformations, and decision points involved in that flow.
How it works in practice
The engineer describes the symptom: 'Shipments from Carrier X are not showing delivery milestones since Tuesday.' The AI searches the relevant codebases, identifies the ingestion pipeline for that carrier type, traces the milestone detection logic, and highlights the most likely failure points based on code analysis and recent changes. If a code change was deployed on Tuesday that modified the milestone detection threshold for that carrier type, the AI surfaces it.
Integration with incident management
The AI-generated trace becomes part of the incident record. When the RCA is complete, the findings (root cause, affected code, fix applied, preventive measures) are structured and stored. Future incidents with similar symptoms can draw on prior RCAs, accelerating resolution over time.
Why this matters
P0 incidents in supply chain visibility have direct customer impact: shipments go untracked, ETAs stop updating, appointments are missed. Every minute of RCA time is a minute of customer-facing degradation. Reducing RCA from hours to minutes is not an engineering convenience. It is a customer experience improvement measured in real operational outcomes.
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One billion hours of operational work completed by AI agents over the next decade.
The supply chains that adopt autonomous execution in the next 24 months will define the competitive standard for the next decade. The ones that do not will spend that decade trying to catch up.
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