AI Agents

Context

Built for logistics 3PLs within the Dynamic Routing stack and used by Transportation / Control Tower teams, this agent takes over once routes have been planned and trucks are on the road. Its job is to keep the plan relevant as reality changes during the day, instead of forcing dispatchers and planners to manually rework routes every time something unexpected happens.

What it does

The agent continuously monitors active routes against live events and triggers re-planning when orders or conditions change during the day. It ingests updated orders, cancellations, urgent add-ons, and operational signals such as delays or missed time windows, then recalculates feasible route adjustments that respect distance, capacity, and time constraints. It proposes updated stop sequences, swaps or reassigns loads between vehicles when needed, and highlights which changes are critical for service (for example, to avoid late arrivals on tight SLAs). Control tower staff can accept or tweak these suggestions, but they no longer start from a blank map whenever something disrupts the plan.

Core AI functions

The core capability is event-driven re-planning: the agent listens for operational changes, evaluates their impact on the current tours, and runs focused routing optimization on the affected parts of the network. Instead of re-optimizing the entire day every time, it targets the minimal set of changes that restore feasibility and protect promised times, turning a stream of small disruptions into manageable, system-guided adjustments.

Problem solved

Static plans can’t handle disruptions. Once the day starts, new orders arrive, some stops run late, others cancel, and traffic or site issues throw off carefully prepared tours. Without a real-time adjuster, this results in constant manual firefighting: dispatchers reshuffle stops by phone and chat, service promises slip, and the original efficiency of the plan erodes hour by hour. The agent replaces that fragmented, manual response with a structured way to absorb changes and keep routes executable.

Business impact

The primary outcome is higher on-time performance. By re-optimizing routes when orders or conditions change, the agent helps keep deliveries within promised windows and reduces the number of late or missed stops. That protects service levels for shippers, supports SLA compliance, and reduces the operational stress of last-minute reshuffles. Over time, this also preserves more of the cost efficiency from the original plan, because mid-day changes are handled systematically rather than through ad hoc rerouting that adds miles and idle time.

Integration and adjacent use cases

Integration complexity is typically in line with other Dynamic Routing agents: the Real-Time Adjuster Agent needs access to the TMS or planning system for current routes, to order and stop updates during the day, and to the telemetry or status feeds that indicate delays or disruptions, plus a way to write back updated tours and flags for exception handling and communications.

Common combinations in this stack:

• Route Optimization Agent to generate the initial route plans from orders, vehicles, and constraints at the start of the planning cycle; and

• Network Insights Agent to analyse historical and real-time route performance, feeding insights about recurring bottlenecks and patterns back into both planning and same-day adjustment rules, while exception management and customer communication flows use the Real-Time Adjuster’s outputs to keep internal teams and shippers informed.