Who's Watching the AI Agents Watching Your Supply Chain? 

In this article, learn about: 

• What always-on AI monitoring provides, and what it doesn't 

• Why an agent that never sleeps still needs a human who's awake 

• The approval fatigue problem hiding inside the human-in-the-loop method 

Retail ops leaders have spent the last two years asking whether AI can watch the supply chain around the clock. That question is largely settled: it can. The harder question, and the one most teams haven't answered yet, is who's on the other end of the alert when it fires in the early hours of a Saturday. 

Always-on monitoring changes when a problem gets found, but it doesn't automatically change when a problem gets fixed. Those are two different capabilities and confusing them is risky. An agent that reviews transactions continuously across a network of trading connections still passes its findings to a person. 

This article walks through the autonomous tiers, the approval fatigue problem that shows up once alert volume outpaces a team's ability to meaningfully review each one, and the questions retail ops leaders should ask before trusting an "always on" claim at face value. 

What Does AI Monitoring Actually Provide? 

Continuous monitoring watches a live network of transactions and flags deviations as they happen, instead of waiting for a person to run a report or catch a problem during a weekly review. That distinction matters more in a supply chain than almost anywhere else, because supply chain problems don't wait for business hours.  

A shipment may get flagged as noncompliant on a Friday evening. An exception that might take five minutes to fix on Tuesday morning compounds for 60 hours if nobody sees it until Monday. 

Does an Agent That Never Sleeps Mean Your Team Doesn't Have To? 

An agent watching your supply chain 24 hours a day still hands its findings to a human being, and that human being has a shift. Gartner's guidance on AI agent governance gives operations leaders a useful way to think about exactly where that handoff sits. Gartner classifies AI agents into four autonomy tiers, and the tier an agent occupies determines what kind of human involvement it requires. 

Related Reading: Agentic AI in Supply Chain: The Day-to-Day Impact 

The Four Autonomy Tiers 

Each step up that ladder changes the human role. At lower levels, people remain responsible for reviewing findings and deciding what happens next. As autonomy increases, oversight shifts toward approvals, exceptions, and the guardrails governing the system itself. Understanding where an agent sits on that spectrum is the first step toward understanding what kind of human coverage it still requires. 

Observe 

At the Observe tier, agents have read-only access. They can retrieve information, summarize documents, identify patterns, or surface something that needs attention, but they cannot change data or take action. Their output is typically delivered to the person who requested it. 

Advise 

At Advise, the agent goes a step further by recommending what should happen next. It might draft a response, flag an issue, or suggest an action, but a human still reviews the recommendation and carries out the action.  

Many monitoring tools, including MAX, operate primarily within the Observe or Advise tiers: The agent finds what matters and brings it to a person to decide what happens next. 

Act with Approval 

The Act with Approval tier gives the agent permission to take action, such as updating data or sending a notification, but only after a human explicitly approves that specific action. This introduces an important governance challenge. Human review only works as a safeguard when people have enough context, time, and attention to meaningfully evaluate what they are approving. 

Act Autonomously 

At Act Autonomously, agents can execute actions independently within defined guardrails. Human oversight shifts away from approving individual actions and toward reviewing exceptions, patterns, and overall outcomes. That requires a different control structure, including clear permissions, continuous monitoring, circuit breakers, and rollback mechanisms when something goes wrong. 

Where Most Monitoring Agents Actually Sit 

The practical takeaway for retail ops leaders: An agent sitting at Observe or Advise is not automatically staffed 24 hours a day just because it runs 24 hours a day. The agent's uptime and your team's uptime are two separate variables, and vendors selling "always on" usually mean the first one. 

Related Reading: How to Leverage AI Solutions in Supply Chain 

Why Approval Fatigue Is a Real Problem 

Always-on monitoring creates a very human challenge: Someone still has to notice the alert and decide what to do with it. That becomes harder to count on when alerts pile up or arrive at 2 a.m. on a Saturday. 

This is the same pattern security teams have fought for decades with warning fatigue, and it's now showing up in agent oversight. Analysis from WorkOS on agent governance describes a threat-detection rule added in March 2026 specifically for approval fatigue exploitation, covering patterns where a person starts rubber-stamping requests because the volume has become unmanageable. Gartner names the same mechanism from the other direction: At the Act with Approval tier, "approvals can degrade under time pressure or approval fatigue, creating a false sense of safety while expanding the attack surface." 

For instance, take a supplier using a dozen agents to monitor supply chain activity around the clock. Those agents might run hundreds of sessions a day, checking orders, shipments, inventory, or compliance issues. That creates a lot of visibility into the business, but it also creates a new question: Who is monitoring the agents?  

As activity grows, teams need a way to know whether agents are running as expected, catching the right issues, and escalating problems when they should. Otherwise, the monitoring system can become another blind spot. 

What Happens When a Retail Exception Sits Unresolved Overnight? 

Retail supply chains generate exceptions constantly: Demand forecasting misses, procurement delays, supplier coordination gaps, inventory replenishment problems.  

Academic research on agentic AI in large supermarket chains describes these as processes that remain "predominantly manual, reactive, and fragmented" even where companies have already invested heavily in data analytics. The decision-making layer (and not the data layer) is where the gap lives. 

SPS Commerce customer Sun & Ski Sports illustrates the directional pattern, even though its setup predates network-wide AI monitoring. The company automated its compliance notification process so vendors are notified by email as soon as a chargeback issue is determined, giving them time to correct it before it compounds instead of relying on someone catching the problem manually.  

That's the shape retail ops leaders should be aiming for: Detection that reaches the right person immediately instead of a detection that produces a report someone reads on Monday. 

What Should You Ask Before You Turn Always-On Monitoring Loose? 

Before adopting a monitoring agent, or auditing one you already have, retail ops leaders can work through a short list of questions that expose whether coverage is actually designed, or just claimed. 

• What autonomy tier does this agent actually operate at? Observe, Advise, Act with Approval, or Act Autonomously. The vendor's marketing language ("always on," "autonomous," "AI-powered") won't tell you this. The tier does.