With decades of experience navigating the complexities of business management and supply chain operations, Marco Gaietti has a unique vantage point on the technological shifts reshaping the industry. Today, we’re delving into the concept of AI Agent Workforces, a new frontier in intelligent automation that promises to move beyond simple assistance to active, autonomous execution. We’ll explore how embedding AI directly into core platforms is changing the game, the synergy between different types of AI agents, and what it truly means for a system to “act, not just assist.”
How does embedding AI agents directly into an operational platform, rather than as an overlay, enhance their effectiveness? Could you walk us through a supply chain scenario where this direct integration allows an agent to take a decisive action that an overlay solution simply couldn’t?
That’s the fundamental difference, and it’s what separates a true operational asset from a glorified dashboard. An overlay solution is like a consultant watching your operations through a window—it can analyze data from a lake, spot a trend, and send you a report flagging a potential problem. It’s passive. An embedded agent, however, lives inside the building. It has access to every real-time event, every system, every piece of operational context. Imagine a ‘Shipment Tracking Agent’ embedded in the platform. It doesn’t just see that a truck carrying a critical customer order is delayed by weather. Because it’s native to the system, it immediately knows the order’s priority, the customer’s service level agreement, and the real-time capacity of other hubs. It can then take decisive action—not just recommend, but act. It might autonomously re-route the downstream leg of the journey through a different carrier, update the ETA for the customer, and adjust labor plans at the destination warehouse, all in seconds. An overlay can only send an alert; the embedded agent orchestrates the solution.
You’ve detailed both Interactive Agents for user assistance and Autonomous Agents for background automation. How might these two types of agents collaborate to solve a complex issue like a potential shipment delay? Please walk us through a step-by-step example of their interaction.
They function as a highly efficient team, blending automated power with human judgment. Let’s stick with our shipment delay scenario. An Autonomous ‘Shipment Tracking Agent’ is constantly monitoring thousands of in-transit orders in the background. It identifies a potential service failure for a high-value client. It automatically analyzes remediation options but recognizes that this particular client requires a high-touch approach. Instead of acting alone, it escalates. It instantly pushes a complete situational brief to the ‘Contact Center Agent,’ the interactive tool used by a human associate. When the associate opens that customer’s file, they aren’t starting from scratch. The agent presents a pre-packaged summary: “Here is the issue, here are three viable solutions with cost and time implications, and here is the recommended course of action.” The human associate can then have an informed, proactive conversation with the customer, make the final decision, and with one click, authorize the autonomous agent to execute the chosen plan. The autonomous agent does the heavy lifting in the background, while the interactive agent empowers the human to be a strategic problem-solver.
The Agent Foundry™ lets clients build custom agents. What does this process look like for a customer creating something like a specialized ‘Dock Agent’? Could you explain the steps they’d take and how the platform ensures it works seamlessly with the core agent workforce?
The Agent Foundry is genuinely groundbreaking because it democratizes this technology. Take the example of Eaton, who collaborated to create a ‘Dock Agent.’ The process doesn’t start with complex coding; it starts with plain language. A logistics manager would define the agent’s purpose: “I need an agent to monitor my dock doors to prevent congestion and prioritize unloading for time-sensitive production materials.” From there, the Foundry provides a guided, low-code environment. You connect the agent to the necessary real-time data streams already flowing through the platform—inbound shipment schedules, trailer GPS data, warehouse labor availability, and inventory levels. Then, you define its logic using a library of platform APIs. It’s like setting up rules: “If an inbound shipment is flagged ‘critical,’ and dock door 4 is scheduled to be free in 15 minutes, reserve that door and alert the ‘Wave Coordinator Agent’ to assign a receiving team.” The key to seamless integration is that these custom agents are built on the same foundation as the standard ones. They use the same A2A and MCP communication standards, so they can talk to, and be understood by, every other agent in the workforce, whether it’s the ‘Labour Agent’ or another custom agent, ensuring they all work in concert.
Early users are using agents like the ‘Labour Agent’ for workforce deployment. What specific real-time data does an agent like this analyze to make its recommendations? Can you share the key metrics a company might track to measure the tangible impact on operational efficiency and resource allocation?
The ‘Labour Agent’ is a fantastic example of moving beyond static planning. It’s not looking at yesterday’s report; it’s looking at what’s happening right now and what’s about to happen. The agent continuously ingests a firehose of datthe exact volume of remaining work in each functional area like picking and packing, the current productivity rate of active associates, the ETA of inbound trucks, and the ticking clock on outbound shipping cutoffs. It synthesizes all this to provide powerful, actionable guidance on where to deploy people next. For a company like Eaton looking to measure the impact, the metrics are incredibly tangible. You’d track ‘Labor Utilization Rate’ to see if idle time is dropping. You’d monitor ‘Order Cycle Time’ to see if products are moving through the facility faster. And critically, you’d look at ‘Cost Per Unit Shipped’ to measure direct financial gains. The ultimate goal is seeing those numbers improve, reflecting a more nimble, efficient, and responsive workforce.
It’s been said these agents “act, not just assist,” moving beyond typical chatbots. What does this mean in practice for a warehouse manager? Could you provide a concrete example of an agent autonomously diagnosing a root cause and orchestrating a workflow to fix it without human intervention?
This is the core of the revolution. For a warehouse manager, a chatbot is an informant. It might say, “Productivity is down 15% in the packing area.” That’s a problem, not a solution. An agent that acts is a partner. Let’s imagine that same scenario. An autonomous agent notices the productivity dip. It doesn’t just flag it; it investigates. It correlates the dip with data from the warehouse control system and discovers the root cause: a specific packing machine is faulting intermittently. It then orchestrates a multi-step solution. First, it automatically generates a maintenance ticket in the enterprise system. Second, it alerts the ‘Labour Agent’ to temporarily reallocate two of the five packers from that line to another active line, preventing a total bottleneck. Finally, it flags the orders from the affected line and recalculates their priority in the outbound shipping queue to mitigate potential delays. The warehouse manager receives a single notification: “Potential bottleneck in packing diagnosed and resolved. Maintenance ticket created. Labor reallocated.” They went from being a firefighter to being a strategic overseer. That’s the difference between assisting and acting.
What is your forecast for the evolution of AI agent workforces in supply chain management over the next five years?
Over the next five years, I believe we’ll see these individual AI agent workforces evolve into a connected, intelligent ecosystem—a central nervous system for the entire supply chain. Today, we’re seeing them optimize operations within a single company’s four walls, like in a warehouse or a contact center. The next frontier is inter-enterprise communication. Imagine a retailer’s agent automatically negotiating with a logistics provider’s agent to secure capacity in real-time, or a manufacturer’s agent coordinating with a supplier’s agent to proactively resolve a component shortage before it ever impacts a production line. The human role will transform from managing tasks to managing outcomes. Professionals will set the strategic goals and guardrails, and then manage a workforce of agents responsible for executing on those goals with incredible speed and efficiency. It will be less about manual intervention and more about strategic orchestration of these powerful digital employees.
