Logistics operations already delegate authority. Dispatchers,
planners, drivers, supervisors, and systems each act within defined
limits. Agent governance should use the same operating discipline.
Five levels of agent authority
The useful question is not whether an agent is “autonomous.” It is
autonomous to do what, under which conditions, using whose data, and
with what consequence? Summarizing yesterday’s exceptions is very
different from changing a customer’s delivery commitment. Governance
becomes practical when it describes that difference in operating
terms.
-
Observe: read approved data and monitor
conditions. -
Explain: assemble evidence and summarize likely
causes. -
Recommend: propose a next action, alternatives,
and tradeoffs. -
Prepare: draft a message, task, plan, or
transaction for review. -
Execute: complete a narrowly defined and
auditable action.
Most first deployments should concentrate on the first four levels.
They reduce analytical and administrative burden while keeping
consequential decisions with a person.
Use risk to set the boundary
Authority can vary by action. An agent may execute one reversible
internal update while remaining recommendation-only for a
customer-facing decision. The boundary should follow the action, not
the technology product or the enthusiasm surrounding it.
Consider financial impact, customer commitment, safety, employment
consequences, reversibility, data sensitivity, and regulatory
exposure. Automatically annotating an internal record is different
from changing a delivery promise or releasing a payment.
Make evidence visible
Reversibility matters. A suggested route change can be reviewed
before dispatch; an automatically released payment or customer
message may be difficult to unwind. Higher-consequence actions
deserve stronger evidence, narrower rules, and explicit approval.
An operator should see why the agent reached its conclusion: which
data was used, what rules were applied, what is missing, and how
confident the system is. A recommendation that cannot be inspected
will struggle to earn adoption.
Human in the loop should identify a real owner with a real
decision—not a generic promise that someone can intervene.
Design escalation as a feature
Evidence should be designed for the person who must act. A
dispatcher may need affected stops, current vehicle position,
remaining capacity, and the triggering rule. An auditor may need the
underlying records and event history. Both views can come from the
same controlled evidence package.
A strong agent knows when to stop. Missing data, conflicting
records, low confidence, unusual financial exposure, safety
conditions, or a policy exception should route work to a named role
with the relevant evidence attached.
Audit the operating result
Escalation should protect attention, not create another queue. Teams
need to define what is material, group related issues, suppress
duplicates, and measure whether escalations are useful. A consistent
human override may reveal a missing rule, a data-quality problem, or
context the workflow has not captured.
Measure acceptance, override, false-positive, escalation,
cycle-time, and outcome rates. Review not only whether the agent was
technically correct, but whether it improved the workflow and
supported responsible judgment.
Start with an authority card
Before a pilot, document the owner, approved data, permitted and
prohibited actions, escalation triggers, retention expectations,
and success measures on one page. Operations, IT, security, and
leadership then have the same concrete operating agreement to
review and improve.
Governance in one sentence
Give each agent the minimum data and authority required for the
use case, make its reasoning inspectable, and keep a named human
accountable for consequential outcomes.