Logistics operations already delegate authority. Dispatchers, planners, drivers, supervisors, and systems each act within defined limits. Those limits are not a constraint on performance; they are part of how a dependable operation works.
AI agents need the same discipline. 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?
That distinction matters because “use AI” can describe very different activities. Summarizing yesterday’s exceptions is not the same as changing a customer’s delivery commitment. Drafting a corrective-action note is not the same as sending it. Recommending a route change is not the same as releasing a new plan to drivers.
Governance becomes practical when it describes those differences in operating terms.
Authority is an operating decision, not a software setting
Organizations often discuss autonomy as though it were a feature that is either enabled or disabled. In practice, authority should be assigned action by action. The same agent may be allowed to complete one low-risk internal task while remaining recommendation-only for a customer-facing decision.
A useful design begins with three questions:
- What is the agent allowed to know? Define the approved systems, records, fields, and time periods it may use.
- What is the agent allowed to do? Separate observation, analysis, preparation, and execution into explicit permissions.
- When must the agent stop? Name the conditions that require escalation to a specific person or role.
This turns governance from a broad policy statement into an operating agreement that leaders, operators, IT, and security can inspect together.
Five levels of agent authority
Most operational AI use cases can be described through five levels of authority. The levels are cumulative in capability, but they do not need to be cumulative in permission. An agent can be highly capable while still being intentionally restricted to observation or recommendation.
1. Observe
The agent reads approved data and monitors defined conditions. It may identify late departures, missed scans, unusual service times, inventory mismatches, or recurring exceptions. It does not explain the condition or propose an action unless that authority has also been granted.
Operational example: monitor planned and actual route activity and flag stops that have crossed a defined service-risk threshold.
2. Explain
The agent assembles relevant evidence and summarizes likely causes. A useful explanation should show its work: the source records used, the rules applied, the information that is missing, and the degree of confidence in the conclusion.
Operational example: explain that a late route was driven by a delayed start, two extended service events, and a customer closure—not simply label the route “inefficient.”
3. Recommend
The agent proposes a next action, alternatives, and tradeoffs. The person responsible for the outcome still makes the decision. Recommendations should be specific enough to act on and should make consequences visible.
Operational example: recommend moving selected stops, adjusting a sequence, or contacting a customer, while showing the expected effect on capacity, service, and downstream commitments.
4. Prepare
The agent drafts the work required to carry out an approved decision. That may include a customer message, a task, a revised plan, a transaction, or a structured record for review. Preparation removes administrative burden without removing accountability.
Operational example: prepare a revised dispatch plan and the related customer notifications, but hold both for supervisor approval.
5. Execute
The agent completes a narrowly defined and auditable action. Execution should not mean unlimited discretion. It should mean that the action, conditions, data, limits, evidence, and recovery path are all clear enough to permit the system to act.
Operational example: automatically annotate an internal exception record when an approved rule is met, while escalating any customer-impacting change for review.
Most first deployments should concentrate on Observe, Explain, Recommend, and Prepare. Those levels can reduce analytical and administrative burden while keeping consequential decisions with a person.
Use consequence to set the boundary
The right level of authority depends on the consequence of the action—not on the sophistication of the technology or the enthusiasm surrounding it.
Before granting execution authority, evaluate:
- Customer commitment: Could the action change a delivery promise, price, service level, or communication?
- Financial impact: Could it release a payment, create a charge, change labor, or commit capacity?
- Safety: Could it affect a driver, employee, customer, facility, or physical asset?
- Reversibility: Can the action be reviewed and undone before it creates an external consequence?
- Data sensitivity: Does it expose personal, customer, employee, commercial, or regulated information?
- Employment impact: Could it influence coaching, scheduling, discipline, hiring, or compensation?
- Regulatory exposure: Is the action governed by a contract, policy, law, or audit requirement?
Automatically tagging an internal record is different from changing a delivery promise. Preparing a payment for approval is different from releasing it. Summarizing a performance pattern is different from making an employment decision.
As consequences rise, the evidence should become stronger, the rules narrower, the approval path more explicit, and the recovery plan more credible.
Make the evidence visible
An operator should be able to understand why an agent reached its conclusion. A polished answer is not enough. The operating evidence should remain available for inspection.
A useful evidence package answers:
- Which source records were used?
- What business rules or thresholds were applied?
- What information was unavailable or conflicting?
- How confident is the system, and why?
- What alternatives were considered?
- What will happen if the recommendation is accepted?
The answer should also fit the person who must act. A dispatcher may need the affected stops, current vehicle position, remaining capacity, and triggering rule. An auditor may need the underlying records, event history, model version, and approval trail. Those views can come from the same controlled evidence package without forcing every user into the same interface.
Design escalation as a feature
A dependable 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.
“Human in the loop” is meaningful only when it identifies a real owner with a real decision. A generic promise that someone can intervene is not an escalation design.
Good escalation also protects attention. If every minor anomaly creates a new alert, the workflow simply replaces one burden with another. Teams should define what is material, group related issues, suppress duplicates, and distinguish an informational exception from a decision that truly needs intervention.
Build a one-page authority card
Before a pilot begins, write down the operating agreement on one page. An authority card gives operations, IT, security, and leadership the same concrete design to review.
For each use case, document:
- Business objective: the operational problem and the intended result.
- Accountable owner: the person or role responsible for the outcome.
- Approved data: the systems, records, fields, and history the agent may use.
- Permitted actions: the exact Observe, Explain, Recommend, Prepare, or Execute permissions granted.
- Prohibited actions: decisions or systems that remain outside the boundary.
- Escalation triggers: the conditions that require the agent to stop and who receives the work.
- Evidence and retention: what must be recorded, displayed, and preserved.
- Success measures: the operational, adoption, and control outcomes that determine whether the pilot should continue.
The authority card should be revised as the team learns. A pattern of human overrides may reveal a missing rule, a data-quality problem, or important context the workflow has not captured. That is useful evidence—not merely a sign that the agent was wrong.
Start with a controlled pilot
A first pilot should be useful enough to matter and bounded enough to understand. Begin with recurring work that consumes expert attention, has accessible evidence, and ends in an outcome the organization can measure.
A practical sequence is:
- Baseline the work. Measure the current volume, cycle time, exception rate, review effort, and operating result.
- Run in observation mode. Let the agent produce evidence without changing the workflow. Compare its findings with experienced operators.
- Add recommendations or prepared actions. Record acceptance, edits, overrides, and the reasons behind them.
- Expand authority only when earned. Consider narrow execution after the evidence, rules, escalation path, and recovery method have proven dependable.
This staged approach creates value early. A team does not need to wait for full autonomy to reduce research, reconciliation, documentation, and preparation effort.
Measure the operating result
Technical accuracy is necessary, but it is not the entire outcome. The agent should improve the workflow and support responsible judgment.
Useful measures may include:
- Recommendation acceptance and override rates
- False-positive and missed-exception rates
- Escalation volume, usefulness, and response time
- Cycle-time reduction
- Operator review effort
- Data-quality defects uncovered
- Customer, service, financial, or operational outcomes appropriate to the use case
Review the measures by action and consequence. A high acceptance rate for internal summaries does not prove that the agent should be allowed to change a customer commitment. Authority should expand because evidence supports the specific action—not because the system performed well somewhere else.
What a well-governed deployment looks like
A well-governed AI workflow is not defined by how much authority it has. It is defined by whether everyone understands the boundary.
The business knows what outcome it is trying to improve. Operators can inspect the evidence. IT and security know what data and systems are in scope. Escalations reach a named owner. Consequential actions retain human accountability. Results are measured, and authority changes only when operating evidence justifies the change.
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.
Put AI to work without losing control
The strongest AI opportunities usually begin with the work—not with the model. Define the business question, the evidence, the operating owner, and the decision boundary first. Then choose technology that fits that design.
If your team is deciding where an AI workflow should observe, explain, recommend, prepare, or execute, Four Square Group can help translate the opportunity into a practical operating model. We bring operational leadership, connected-system thinking, analytics, and implementation discipline to the work.