About this case study
This is an illustrative, composite engagement pattern reflecting how we structure dispatch-automation work — not a named client account. Details have been generalized to protect confidentiality; the approach and figures are representative of engagements of this shape.
The starting point
The client operated a regional freight dispatch desk coordinating deliveries across several hundred routes a day. The core problem wasn't volume — it was exceptions. A late pickup, a damaged pallet, a carrier substitution: each one landed as a ticket in a shared queue, and a dispatcher had to read the context, decide what to do, and manually notify everyone affected. On a normal day, dispatchers were spending the majority of their time on exceptions that followed genuinely consistent logic, not judgment calls that needed a human every time.
That pattern — a small number of workflows generating a disproportionate share of manual work, with decisions that follow a consistent playbook — is exactly where agentic automation tends to pay off fastest in logistics, and exactly where a chat-based tool bolted onto the existing ticket queue would have missed the point.
Why a queue-based fix wasn't enough
The client's first instinct was a smarter ticketing system — better routing rules, more filters. We pushed back: the bottleneck wasn't finding the right ticket, it was the manual reasoning inside each one. A better queue would have organized the same amount of manual work more neatly. The fix needed to touch the decision itself.
What we built
- An exception-intake agent that reads incoming exception reports — from carrier APIs, driver messages, and warehouse scans — and classifies them against a fixed taxonomy of known exception types.
- A resolution-drafting step where the agent proposes the standard next action for that exception type — reroute, rebook, notify the customer — citing the specific data it used to decide.
- A dispatcher approval checkpoint for anything above a defined cost or customer-impact threshold; routine, low-stakes exceptions resolve automatically with a logged rationale.
- A weekly review loop where dispatchers flag any agent recommendation that was wrong, feeding directly back into the exception taxonomy.
Where the human stayed in the loop
Every automatic resolution was reversible and logged. Dispatchers kept full visibility into what the agent decided and why, and anything involving a customer-facing commitment — a delivery promise, a compensation decision — still required sign-off. The goal was never an unattended dispatch desk; it was giving dispatchers their attention back for the exceptions that actually needed it.
The taxonomy did the real work
The agent's reasoning was only as good as the exception taxonomy underneath it. Two weeks of the engagement went into cataloguing exception types with the dispatch team before any automation was built — time that paid for itself several times over once the agent was live.
Illustrative outcomes
Shipment-exception agents of this kind typically bring review time per exception down from the 15–30 minute range to under five minutes, with overall exception-handling time reduced by 60–80%. For this engagement, that translated into dispatchers reclaiming the majority of a day previously spent on routine exceptions, redirected toward carrier relationships and the genuinely ambiguous cases that still need a person's judgment. The engagement — taxonomy, agent, and dispatcher workflow — ran in the range of 10–14 weeks, consistent with similar exception-handling builds.
The agent didn't remove the dispatcher from the loop. It removed the fifteen minutes of reading before the dispatcher could even start deciding.
What we'd tell a team starting this today
- Build the exception taxonomy with the people who currently handle exceptions, before writing any agent logic — it's the highest-leverage two weeks in the engagement.
- Start automatic resolution at a conservative cost/impact threshold and widen it as trust in the agent's track record grows, not the other way around.
- Log every automatic decision with its rationale — the weekly review loop is what keeps the taxonomy (and the agent) improving after launch.
Recognize this pattern in your own dispatch or operations queue? See how we build agent workflows, or talk to us about where the exceptions are piling up.