From Alerts to Action: How We’re Building Agentic Operations That Actually Scale

By Kris Glotzbach, Chief Revenue Officer, LunaPath

As I prepared for the recent webinar with project44, I spent time reflecting on the questions I hear most often from supply chain leaders:

What actually makes AI agents different from the last wave of automation? Why does adoption stall in so many organizations? And why does this moment feel different?

The short answer is this: we’ve reached an inflection point where visibility alone is no longer enough. The next phase of value comes from orchestrating what happens after an event occurs.

That belief is at the core of how we built LunaPath.

What Makes LunaPath Different

LunaPath was purpose-built around internal operational excellence, not surface-level visibility. Most systems do a good job of identifying problems:

• A late load

• A missing update

• A broken handoff

What they don’t do well is coordinate what happens next. LunaPath focuses on exception management, cross-team coordination, and prescriptive resolution. Instead of generating more alerts, it actively orchestrates work across people, systems, and stakeholders in response to live operational events.

The goal is simple: reduce chaos, not create more noise.

By connecting communication and coordination across systems, LunaPath turns manual, reactive work into structured, repeatable execution. That shift allows teams to move from constant firefighting to controlled, scalable operations.

Why Foundations Matter More Than Features

The biggest opportunity I see in deploying and scaling AI agents is not model sophistication, but rather data discipline.

When organizations invest in:

• Standardized workflows

• Clean operational data

• Strong governance

AI becomes dramatically more reliable.

High-quality data creates a trust layer. That trust allows agents to act with confidence and enables teams to rely on automation without fear of losing control. This isn’t theoretical. Companies like C.H. Robinson invested early in standardizing operational data and treating governance as a core capability. That work is now paying off through faster AI adoption and better operational outcomes. They recently highlighted AI as a material driver in earnings; clear evidence that data discipline has become a competitive advantage.

The Integration Landscape Is Finally Catching Up

Integrations have historically been a bottleneck, but the trend is encouraging. APIs are more mature, data models are becoming more consistent, and event-based architectures are improving interoperability across TMS, WMS, ELD, and ERP platforms.

It still takes work, but the ecosystem is moving in the right direction, and that progress makes it far easier to scale AI agents across complex supply chain environments.

This is one of the reasons agentic automation is viable now in ways it wasn’t even a few years ago.

Why Change Management Is the Real Work

One of the most underestimated challenges in AI adoption isn’t technology - it’s people. Operators have spent years building tribal knowledge, manual workarounds, and role-specific expertise that keep operations running. That knowledge is incredibly valuable, but it can also create hesitation when automation is introduced.

People worry about losing:

• Control

• Relevance

• Visibility

That concern is valid, and it’s why positioning matters. The teams that succeed position AI agents as an augmentation layer, not a replacement. They invest in:

• Training

• Clear operating models

• Defined escalation paths

• Transparency around why an agent acted

When operators can see why an agent made a recommendation, and when they know exactly when a human steps in, trust builds quickly. Once that happens, adoption accelerates. Teams feel the impact in their daily work. Productivity improves. Service levels rise. Decisions happen faster. And organizations scale output without scaling headcount.

At that point, skepticism turns into advocacy.

Where Agent Collaboration Is Headed Next

Over the next 6-12 months, the most important shift will be agent collaboration through orchestration. Instead of single agents working in isolation, we’ll see orchestration layers behave like control towers:

• Monitoring streams of operational events

• Detecting delays, exceptions, or threshold breaches

• Dynamically handing work off to the right specialized agent

This evolution is being driven by event-based architectures. Agents subscribe to normalized shipment, inventory, and status events and respond in real time rather than relying on scheduled jobs.

The result is:

• Faster cycle times

• Lower operational friction

• More autonomous workflows

• Humans still firmly in the loop for high-impact decisions

Why Now

The reason this moment matters is simple. Logistics has reached a real inflection point:

• The data infrastructure is finally ready

• APIs and cloud platforms are mature

• Margin pressure has made headcount-driven execution unsustainable

Point solutions and alert fatigue are no longer enough. Teams don’t need more tools, rather they need intelligent systems that orchestrate work across the supply chain and move beyond alerts into action.

Where Customers See the Most Success with LunaPath

Customers see strong results with LunaPath because it was built as a platform, not a point solution. It was designed by operators who understand how brokers, shippers, carriers, warehouses, and managed TMS providers work in real conditions. That experience shaped LunaPath into an orchestration layer rather than another silo.

When exceptions occur, LunaPath doesn’t just surface them, it recommends and executes next-best actions. It meets stakeholders where they already transact, operating inside TMS, WMS, ELD platforms, browser-based tools, email, portals, and messaging environments.

The biggest wins consistently show up in:

• Exception management

• Cross-system orchestration

• Proactive disruption response

That’s where friction is removed, resolution accelerates, and measurable improvement shows up on the P&L.

AI agents are not about replacing expertise. They’re about removing the friction that prevents that expertise from being applied where it matters most. When automation is designed around operational reality, governed by data discipline, orchestrated across systems, and built to augment people, it doesn’t just scale work. It changes how organizations operate.

And that’s the opportunity in front of us right now.