Layer 02 Agentic Intelligence

Agentic AI

AI that decides and acts on its own within defined guardrails, not just recommends. In logistics, an agentic system evaluates conditions and executes, rerouting a load or resolving an exception, rather than surfacing a suggestion for a person to approve. Agentic describes how a decision gets made; autonomic describes a system that runs continuously. Nash is autonomic, and agentic action is one mechanism inside it.

Where this fits

Layer 02 of the Nash platform: the agents that observe, analyze, and act. Ask Nash for natural-language questions, custom workflow agents triggered on real events, durable agent memory across runs. Where the system makes decisions about decisions.

Related terms

Workflows: Order-level routing logic. Decides which dispatch strategy applies, which modules are invoked, what happens when conditions change.
Agents: Autonomous actors that pursue goals you set, e.g. keep SLA above 95% or re-dispatch any order unaccepted after 4 minutes. Pre-built or operator-configured.
Memory: The learning substrate. Provider scores update, routing improves, promise accuracy tightens with every outcome. Skills turn operational know-how into reusable playbooks.
Operational alerting: The signal an operator gets the moment something needs attention: a delivery running late, a provider that just canceled, an ETA sliding past its window. Nash's agents raise it against live context and route it to whoever can act, so problems surface before a customer notices.
Automatic failover: When a delivery breaks, the reassignment that happens without a person. A provider cancels or a driver goes dark; Nash detects it, scores the alternatives, and moves the order to the next best option before the promise is at risk. Self-healing, applied to one order in flight.