The Inside Track: Workflow Engine + Agentic AI: How Tive Automates the Hard Part of Logistics

Every visibility platform on the market can tell you when something goes wrong. That part’s solved. The part nobody’s cracked yet is what happens after the alert gets sent.

I've always been drawn to thinking critically about how new products can deliver value in compelling markets—and I joined Tive because I saw a gap that kept growing. Teams collect more data than ever before, but they're still struggling to act on it. They still wake up at 2:00 a.m. to make calls and run down escalation lists.

My team builds the software that turns Tive’s ground truth shipment data into shipment intelligence and beneficial outcomes. And right now, we’re focused on our workflow engine and the role agentic AI plays in it.

Other companies are chasing workflows, too. But a workflow without context is just automation going through the motions. What makes an AI agent effective is knowing what to say, when to say it, and why it matters for that specific shipment. Tive has a decade of ground-truth data that gives our agents that edge.

I want to pull back the curtain on what we’re building—and why we think it changes the game.

The Fundamentals

Before I get into the specifics of what we’re building, I want to lay out the thinking behind it. Understanding why we built our workflow engine the way we did starts with understanding where the industry stands today, and knowing where the real opportunity lies.

Why Now? Visibility Has Matured; Now the Bottleneck is Response

The industry can see what’s happening to shipments in real time; temperature, location, light exposure, and shock are all there.

But knowing and doing are two different problems. Visibility platforms often surface the issue, then leave the customer holding the bag. Someone still has to interpret the alert, decide if it’s real, and execute the next steps manually.

That response window is where ROI gets impacted. Spoilage is prevented, or it isn’t. Theft gets stopped, or the cargo disappears. On-Time In-Full (OTIF) stays intact, or a customer relationship takes a hit.

Manual response doesn’t scale, shipment volumes keep climbing, and risk profiles keep shifting. Theft tactics evolve to get smarter every quarter.

The best ops teams already run on SOPs for these moments. At Tive, we saw the opportunity to encode those SOPs into a workflow engine—to make the response repeatable, fast, and consistent across every shipment.

Why Agentic AI Changes the Game (and Why “Workflow-Only” Isn’t Enough)

A workflow engine can trigger actions. Agentic AI can conduct them.

Think about what happens when something goes wrong mid-shipment. Traditional automation might fire off an email or create a ticket. An agentic AI operates more like a capable team member: it calls the driver, asks the right questions based on what the sensors show, captures proof, updates status, and escalates intelligently when the situation calls for it.

But here’s the catch: a voice agent is only as good as what it knows in the heat of the moment. What to say depends entirely on context. Shipment details, exception history, customer SOPs, commodity sensitivity, lane norms, live sensor data—all of it matters.

Some providers automate check calls or workflows as standalone solutions. Without deep real-time shipment visibility, sensor truth, and historical patterns, those agent actions become generic or error prone, failing to fully resolve the issue autonomously.

Context makes the difference between an agent that helps—and one that ultimately has to escalate to a human to complete a task.

What “Workflow Engine” Really Means at Tive

A workflow engine connects three things: triggers, conditions, and actions. Something happens, the system checks if certain conditions are met, and then it executes a response. So when an alert fires at 2:00 a.m., the workflow runs and handles the issue—or escalates it intelligently.  

While many solutions stop at visibility, Tive’s workflow engine goes further—providing the infrastructure teams need to fully automate response sequences at scale. The pieces fit together like this:

• Triggers and conditions: Location events, temperature alerts, light exposure, unexpected stoppages, lane anomalies, destination arrivals: these moments kick off a workflow and determine what happens next.
• Actions that go beyond email blasts: Calling drivers, requesting photo proof, adjusting real-time tracking behavior, starting or completing shipments, escalating to the right person. Your workflow can do what your best operator would do, just faster and at any hour of the day or night.
• Infrastructure paired with templates: The engine is the foundation. The workflows built on top are the repeatable sequences. One gives you flexibility; the other gives you speed. You need both.
• Plug-and-play logic: Teams can string together conditions and actions, test them, and iterate without starting from scratch. No one wants to rebuild the wheel every time a new risk or SOP must be addressed.

The Highest-Impact Workflows Start Where Risk is Highest

The Tive team could have started anywhere with our workflow engine. We chose cargo theft and temperature because those two risks cost our customers the most when response is slow. Experienced teams already have SOPs for these moments; our workflows mirror what those teams do today, just more quickly, more consistently, and with less dependence on someone being awake and watching a screen.

Theft Escalation: When Every Minute Counts

Cargo theft is a timing game: the faster you respond, the better your odds of recovery. 

A theft escalation workflow kicks off when conditions stack up: a shipment stops unexpectedly, a light event is detected (someone opened a door), the location doesn’t match any known or authorized stop, or some combination of other risk signals.

Once those conditions trigger, the workflow engine takes over. An agentic voice agent attempts to make contact with the driver and asks contextual questions. What’s happening? Were doors opened? Was the stop expected? The agent requests photo proof to verify the situation.

If everything checks out, the workflow logs the outcome, de-escalates, and continues monitoring. If verification fails or the driver doesn’t respond, the workflow escalates to a monitoring lead, security team, or law enforcement based on that customer’s SOP. 

The workflow then closes when the risk resolves or is handed off with full context if a human needs to step in.

Temperature Excursions: Protecting the Cold Chain

Cold chain shipments don’t give you much room for error.

A temperature excursion workflow watches for readings that exceed a threshold and then waits a set number of minutes. Sometimes a brief spike corrects itself. If the temperature stays high, the workflow calls the driver to check reefer settings and requests photo confirmation.

Unresolved? The workflow escalates according to your SOP. Resolved? The workflow automatically closes when the temperature returns to the stated range and remains stable.

No one has to keep an eye on a dashboard or remember to follow up.

What Sets Tive Apart

Workflows and agentic AI sound great on a slide deck. Every vendor in our space will tell you they have automation and intelligence baked in. The difference comes down to what’s behind the curtain. We built our workflow engine on top of capabilities that took years to develop—and those capabilities give our AI agents the context they need to perform in the real world.

Ground Truth Data and a Decade of History

Tive owns the full stack. Our hardware and software work together to deliver ground truth data about what’s happening to shipments. We’re not passing along status updates from third parties. We capture sensor-backed reality: location events, condition readings, timing patterns, and anomaly signatures.

Ten years of shipment history makes that data even more valuable.

We know lane behaviors, facility patterns, and what “normal” looks like for specific routes and commodities. That baseline gives our AI agents the ability to prioritize which events matter, choose the proper escalation path quickly, and ask situational questions instead of generic ones. An agent calling a driver about a stop in rural Arizona can reference whether that location has been a problem before, or if stops there are routine.

We built agentic workflows that work in the messiness of the real supply chain because we have the context to back them up.

Where We Go From Here

My team didn’t set out to build another visibility tool that fires alerts into the void: we wanted to build something that finishes the job. The workflow engine and agentic AI we’ve put together represent where I believe this entire industry needs to go: less staring at dashboards, and more problems getting handled before anyone has to lose sleep over them.

We’re not the only company talking about automation right now. But at Tive, we have something most don’t: 10 years of ground-truth shipment data and the hardware-software integration to back it up. Our AI agents know what’s happening, why it matters, and what to do next: that’s the difference between a generic check call and one that moves the needle.

If you want to see what response on autopilot looks like, get started with Tive today.