What AI Agents Actually Do (Without the Hype)

"AI agents" is the hottest term in enterprise software right now. Every vendor is slapping it on their product. But what does it actually mean for food processing operations?

Let's cut through the hype.

What AI Agents Are Not

First, let's clear up some misconceptions:

Not chatbots. A chatbot answers questions. An agent takes action. Big difference.

Not dashboards with AI labels. Showing you a chart with "AI-powered insights" isn't agentic. That's analytics—useful, but not what we're talking about.

Not robots. AI agents are software, not hardware. They work with your existing systems, not physical machines (though they can send instructions to those machines).

What AI Agents Actually Are

An AI agent is software that can:

1. Perceive — Take in data from multiple sources
2. Reason — Analyze that data against goals and constraints
3. Decide — Choose a course of action
4. Act — Execute that decision (or recommend it for human approval)
5. Learn — Improve based on outcomes

The key difference from traditional software: agents don't just follow rules. They consider context, weigh tradeoffs, and handle situations they weren't explicitly programmed for.

A Concrete Example: Production Scheduling

Let's make this real. Here's how a traditional system handles production scheduling vs. an AI agent:

Traditional approach:

• Pull inventory data from ERP
• Pull orders from order management system
• Apply fixed rules (oldest inventory first, highest-priority customer first)
• Generate schedule
• If something changes, human replans manually

Agentic approach:

• Continuously monitor inventory, orders, line status, labor availability, quality data, and historical performance
• Understand that "oldest inventory first" might not be optimal if that inventory has quality issues
• Recognize that a customer marked "high priority" might actually be flexible this week based on conversation history
• Balance throughput goals against changeover costs against quality preservation
• Generate schedule with reasoning: "I scheduled Honeycrisp before Gala because the Honeycrisp has 3 days shorter remaining shelf life and the Costco order can absorb the full lot"
• When conditions change, automatically recalculate and surface the impact: "Line 2 breakdown adds 4 hours to the Walmart order. Here are three options to recover."

The traditional system follows rules. The agent pursues goals.

Why Now?

AI agents aren't new as a concept. So why are they suddenly everywhere?

Three things changed:

1. Large Language Models got good at reasoning. GPT-4 and its successors can understand context, weigh tradeoffs, and explain their reasoning in ways that previous AI couldn't. This matters because operations decisions require nuance.

2. Integration got easier. Modern APIs and data platforms mean agents can actually connect to your systems without massive IT projects. Five years ago, getting real-time data from Famous ERP required a custom integration. Now we can do it in weeks.

3. Compute got cheap. Running the kind of optimization calculations that agents need used to require expensive dedicated hardware. Now it's a cloud service that costs pennies per decision.

The Human-in-the-Loop Question

"So the AI just makes all the decisions?"

No. And this is where a lot of the hype goes wrong.

Good AI agents are designed with human-in-the-loop. They:

• Suggest decisions for human approval
• Explain their reasoning so humans can evaluate
• Escalate unusual situations to humans
• Learn from human overrides

At Terrantic, our agents start in "suggest mode"—they generate plans and recommendations, but humans approve everything. As trust builds, operators can selectively enable autonomous execution for routine decisions while keeping human approval for exceptions.

The goal isn't to replace your planners. It's to handle the 80% of routine work so your planners can focus on the 20% that actually requires human judgment.

What to Look For

If you're evaluating "AI agent" solutions, here's what to ask:

1. What actions can it take? If the answer is "it shows you insights," that's analytics, not an agent.
2. How does it handle edge cases? Agents should gracefully escalate to humans when they encounter situations outside their training.
3. Can you see its reasoning? Black-box decisions are dangerous in operations. You need to understand why the agent made a recommendation.
4. How does it learn? Does it improve based on outcomes? Can it learn from your specific operation's patterns?
5. What's the human-in-the-loop model? How much autonomy does it have? How do you adjust that over time?

The Bottom Line

AI agents are real, they're powerful, and they're ready for food processing operations. But they're not magic, and they're not autonomous overlords.

Think of them as extremely capable assistants who can handle complex, repetitive decision-making—freeing your human experts to focus on strategy, exceptions, and improvement.

That's not hype. That's just good software design, finally made possible by advances in AI.