Agents here, agents there, agentic this, agentic that. We’ve all heard it a million times this past year. You’d think by now everyone would understand what agents actually are, right? Well, turns out it’s not that simple!

When you read about AI agents, they sound like the ultimate panacea for corporate woes. But reality hits hard when you try turning those grand promises into something tangible. I’ve noticed this firsthand while discussing the technical nuts and bolts with colleagues and customers—their reactions often surprise me. Here’s how I break it down:

An AI agent is the LLM-powered interaction and orchestration layer slapped on top of boring old automation workflows.

What does that mean? It means you still have to do the hard work: defining workflows, implementing API calls, and handling all the nitty-gritty before adding that shiny “intelligence” layer. No shortcuts. No “just describe what you want and… MAGIC.” Nope. At its core, this is still business process automation—with all its challenges and complexities.

Don’t get me wrong—AI agents do have upsides. LLMs excel at parsing user intent, which makes agents great at guiding users through processes, handling ambiguity, and cleaning up messy inputs. They’re also flexible in deciding when to call specific APIs. Let’s face it: chatting with an agent beats filling out yet another soul-crushing input form.

But here’s the catch (and it’s a big one): AI agents are non-deterministic by nature. Thanks to how LLMs work, their behavior isn’t fully predictable. They’re black boxes, making troubleshooting a nightmare when outputs go sideways. Plus, slapping that “smart” layer on top? It cranks up the complexity and cost of your solution. You’ve got to ask: Does the added flexibility outweigh the headaches?

There’s no denying the buzz in this space—multi-agent orchestration, MCP, NLWeb, and other developments are worth watching. But don’t let hype blind you. Focus on maximizing utility with tools that are actually robust.