Agentic dispatch system that cut exception handling time by 74%
A US-based freight operator was handling thousands of daily exceptions manually. We built an autonomous dispatch agent that detects, classifies, and resolves exceptions without human intervention.
Agentic AI Systems, in production.
What stood in the way.
Dispatch teams were drowning in exceptions: missed pickups, address mismatches, capacity conflicts, and carrier failures that each needed a human to read, interpret, and resolve. The manual queue grew faster than the team could clear it, and resolution quality varied with whoever happened to pick up the ticket.
How MetaSys built it.
Mapped the full taxonomy of exception types and the decision logic an experienced dispatcher applies to each one.
Built an agent that ingests every exception, classifies it, and either resolves it directly through connected systems or routes the genuine edge cases to a human with full context attached.
Instrumented the agent with an evaluation pipeline so decision accuracy is measured continuously and drift is caught before it reaches operations.
Deployed behind a human-in-the-loop gate, then progressively widened the agent's autonomy as accuracy held above target.
What changed for the business.
The agent now clears the majority of exceptions on its own, freeing the dispatch team to focus on the cases that genuinely need judgment. Manual exception volume fell sharply and resolution became consistent rather than dependent on who was on shift.
The MetaSys capabilities behind this build.
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