AI Agents

Context

Built for logistics 3PLs within the Dynamic Routing stack and used by Transportation / Analytics teams, this agent looks at how your network actually performs over time. Its job is to analyse route performance data and pinpoint recurring bottlenecks—lanes, customers, time windows, depots, or carriers that systematically drive cost and delay—so you’re not just planning and replanning, but learning and improving.

What it does

The agent ingests historical and near-real-time route data from your TMS and telematics: planned vs. actual routes, stops, service times, delays, missed windows, distance, and utilisation. It groups and compares performance across lanes, territories, customers, carriers, and time bands, then highlights patterns such as chronically late sequences, repeatedly under-utilised vehicles, problematic depots, or stops that consistently blow their slot. For each hotspot, it surfaces a simple, actionable view—where it happens, how often, and what it costs in miles, time, or SLA impact—so network and planning teams can decide what to change in the model rather than fighting the same fire every week.

Core AI functions

The agent combines analytics and anomaly detection: it runs descriptive analytics to establish baselines for “normal” performance by lane, route type, and customer, then applies anomaly detection to find outliers and chronic problem areas. It scores and ranks these inefficiencies by impact, so your teams focus on the bottlenecks that matter most instead of sifting through raw reports.

Problem solved

In many 3PL operations, chronic inefficiencies simply persist. Planners and dispatchers see the same late lanes and overloaded routes every week, but insights are trapped in local knowledge or static reports no one has time to interpret. Network design and carrier decisions are made on partial data, so structural issues stay in place while people firefight symptoms. This agent turns that scattered experience into a consistent, data-driven view of where the network underperforms.

Business impact

The primary impact is continuous cost and service improvement. By systematically surfacing recurring bottlenecks, the agent helps you reduce avoidable miles and overtime, improve on-time performance on problematic lanes, and make better carrier and lane strategy decisions using evidence rather than anecdotes. Over time, that supports tighter SLAs, healthier carrier scorecards, and a routing operation that steadily gets leaner instead of drifting back into old patterns.

Integration and adjacent use cases

The agent typically reads planned vs. actual route, stop, and event data from your TMS and telematics/mapping layer, then writes its insights, rankings, and hotspot flags back into analytics dashboards or planning workspaces—no change to dispatch workflows required.

Common combinations in this stack:

• Route Optimization Agent to feed updated parameters and constraints (e.g., time windows, service times, territory boundaries) based on the bottlenecks identified, and

• Real-Time Adjuster Agent to use those learnings when re-optimising live routes so that day-of-operation changes don’t keep reinforcing the same structural issues. Outside the stack, the Network Insights Agent also underpins carrier scorecards and lane strategy work by providing objective, route-level performance signals over time.