Balancing agentic AI with human judgment

In a recent post, I discussed the need to upskill diesel technicians to keep pace with rapidly emerging technologies. One of the biggest forces driving that change is artificial intelligence (AI), often viewed as a silver bullet capable of streamlining processes, cutting costs, and improving accuracy and efficiency.

Now, with the rise of agentic AI, that perception is intensifying. Business leaders may feel increasing pressure to adopt these systems broadly, trusting them to handle complex workflows with minimal oversight. However, relying too heavily on this technology would be a mistake.

To understand why, it’s important to distinguish between generative AI (the technology we are familiar with) and its more autonomous counterpart.

Agentic AI vs. generative AI in fleet operations

According to Red Hat, an open hybrid cloud technology leader, “Generative AI creates context like text, images, or code in response to prompts.”

Agentic AI takes things further.

Red Hat continues, “Agentic AI acts as an autonomous system that plans, makes decisions, and executes multistep workflows using external tools to achieve a specific goal without continuous human intervention.”

At a recent NationaLease meeting, Armando J. Perez Carreno, principal software engineer at PerezCarreno & Coindreau, clarified the distinction between standard workflow automation and agentic AI.

Many fleets today already use workflow automation tools enhanced by AI. These systems streamline processes, improve visibility, reduce errors, and enhance efficiency; however, they don’t make major decisions. That responsibility remains firmly in human hands.

For example, in warehouse operations, automation and AI already play essential roles. Machines handle repetitive, high-volume tasks with precision, while human workers verify order accuracy, resolve discrepancies, and ensure customer satisfaction. This balance works because it combines speed and consistency with human judgment.

How agentic AI can improve fleet maintenance workflows

As Perez Carreno explains, agentic AI has the “same shape as a workflow but with judgment in the middle.” That added layer of reasoning introduces new capabilities and new risks.

Consider the following real-world fleet scenario:

• A technician records voice notes about a vehicle issue.
• Agentic AI converts those notes into a clean work order narrative.
• It flags potential warranty opportunities.
• It cross-references the unit’s history.
• It then suggests a likely root cause.

This represents a leap forward in efficiency and insight. It saves time, reduces administrative burden, and surfaces patterns that might otherwise be missed.

But this is exactly where caution is required.