AI Agents

Context

Built for logistics 3PLs within the Dynamic Routing stack, this agent sits in Transportation / Planning and focuses on day-to-day route design. It is used when planners build linehaul or last-mile runs, where they need to respect distance limits, vehicle capacity, and delivery time expectations across dozens or hundreds of stops.

What it does

The agent calculates efficient routes under distance, capacity, and time constraints, turning a pool of orders and vehicles into concrete, executable tours. It ingests orders with locations and time windows, vehicle profiles and shifts, depot locations, and basic service rules, then proposes route plans that meet those constraints while minimizing total miles and travel time. Planners can adjust business rules or priorities, and the agent re-runs the optimization to produce updated plans that remain realistic for drivers and customers.

Core AI functions

At its core, this is routing optimization: the agent solves multi-stop vehicle routing problems under capacity and time constraints, balancing hard limits (vehicle capacity, maximum shift length, delivery cut-offs) with soft goals like fewer miles, fewer vehicles, or better stop sequencing. It continuously evaluates alternative combinations of orders and vehicles, scores them on cost and service, and converges on a plan that fits within operational limits while improving on manual baselines.

Problem solved

Inefficient routes drive cost and delays. Without a dedicated optimizer, planners either over-constrain themselves with simple rules of thumb or rely on legacy tools that cannot handle today’s order volumes and tight windows. The result is excess miles, under-used vehicles, late stops at the end of long runs, and constant manual tweaking on the day of operation. This agent gives them a consistent, data-driven way to design routes that respect real-world constraints from the start.

Business impact

The primary impact is lower miles and faster deliveries. More efficient tours cut fuel and toll spend, reduce driver hours required to move the same volume, and shrink the number of trucks needed on peak days. Better-designed routes also improve on-time performance and predictability for shippers, which in turn strengthens SLA adherence and customer satisfaction. Over time, the agent becomes a standard way of working for planners, not an exception tool.

Integration and adjacent use cases

The agent typically reads orders, locations, and constraints from your TMS or order management systems, uses master data for vehicles and depots, and writes the optimized routes and stop sequences back into the same planning or dispatch environment. Telematics or mapping services are used for drive-time estimates but core systems do not need to be replaced.

Common combinations in this stack:

• Real-Time Adjuster Agent (Logistics – 3PLs / Dynamic Routing) to re-optimize routes when orders, traffic, or weather change during the day; and

• Network Insights Agent (Logistics – 3PLs / Dynamic Routing) to analyze historical route performance, pinpoint recurring bottlenecks, and inform long-term lane and territory design.