Design Agero Dispatch Optimization

Product Context

Agero operates roadside assistance and towing dispatch through surfaces used by dispatch agents, service providers, and drivers. Design an end-to-end ML-driven dispatch optimization system that recommends which provider and route should be selected for each incoming roadside event, balancing ETA, completion likelihood, cost, and member experience.

Scale

Task

1. Clarify the product objective and define the optimization target across ETA, acceptance, completion, and cost.
2. Design a multi-stage ML system for candidate retrieval, ranking, and re-ranking / constrained optimization.
3. Specify the offline and online data pipelines, feature store design, and training cadence.
4. Propose model choices for each stage, including how routing signals and provider behavior are incorporated.
5. Define evaluation, experimentation, monitoring, and rollback strategy.
6. Identify major failure modes, including feature drift, training-serving skew, and operational outages.

Constraints

• The system must support real-time dispatch decisions for high-severity roadside events where latency directly affects customer wait time.
• Some labels are delayed or censored: true completion and final ETA error may arrive 30-120 minutes later.
• Provider availability, traffic, and job queues change minute to minute, so stale features are costly.
• The system must remain explainable enough for Agero dispatch operations to override recommendations when needed.
• Compliance and contractual rules may constrain which providers can service certain geographies, vehicle types, or OEM programs.