Recently, it seemed that you couldn’t scroll through your technology feed without running into the phrase “Agentic AI” It’s all over the place — in webinars, whitepapers, linked-in feeds and conversations about the next big jump beyond Generative AI. I completely get it about the excitement around innovation, but part of me just can’t help but question if we’re seeing some “old wine in a new bottle”

Don’t get me wrong, the achievement in AI, especially with Large Language Models (LLMs), is amazingly astonishing. They’ve opened doors we barely imagined a few years ago. But every time we start applying the “agentic” term to any system showing a glimmer of sequential behavior or conditional logic, it does feel little bit like rebranding.

What’s All the “Agentic” hype all about?

Essentially, “Agentic AI” essentially refers to systems that can:

Perceive: Get information from the world around them.
lan: A group of tasks to accomplish a goal.

Act: Do those actions, typically with the assistance of (e.g., APIs, code interpreters, or third-party services).

Reflect/Learn: Change future actions according to outcome or feedback.

Sounds great, huh? And it certainly can be applied to automating multi-step processes.

But Haven’t We Seen This Before?

That is where my “old wine” attitude kicks in. If you have been around for years doing software development, maybe in areas like enterprise architecture, workflow automation, or even robotics, these concepts are not that new.

• Intelligent Agents: This is an old idea in AI research. We’ve had theoretical arguments and real implementations of “agents” — self-contained systems that exist in an environment to achieve something — for decades. Think about early expert systems, rule-based systems, or even as basic as a well-tuned feedback loop in a control system. They all had elements of perception, decision, and action.

• Workflow Orchestration & Automation: Businesses have been automating complex processes for decades. Business Process Management (BPM) suites, Robotic Process Automation (RPA), and tons of ad-hoc scripts have been automating multi-step workflows, calling APIs, and reacting to data changes.

• Software Design Principles: Good software, independent of the AI label, will often use methods that make it “agent-like”: state machines, event-driven design, and modules designed for specific responsibilities and interactions.

What’s actually new with today’s “Agentic AI” is the natural language interface and reasoning abilities of LLMs that enable these systems to learn high-level objectives and frequently infer the steps, instead of being specially programmed for every single case.
That’s a tremendous improvement in usability and flexibility, to say the least. But the fundamental ideas of autonomous action, planning, and tool use? They’ve been on the engineer’s shelf for quite some time already.

Why the Relabeling?

Maybe it’s the cycle of technology marketing — discovering a fresh, cool word for a variation of old concepts to get noticed, raise funds, and make products stand out. “Intelligent Automation” became “RPA,” then “Hyperautomation,” and now, maybe, “Agentic AI” is the next thing. It sounds more advanced, more powerful, more — agentic :-) :-) .

The danger here isn’t the technology, which is promising smarter automation. The danger is the hype generating unrealistic expectations. If companies or individuals expect an entirely autonomous, error-free digital worker who can be commanded with a simple word, they’ll be disappointed. My experience, and that which I hear from many others, is that these systems, as capable as they are, still require plenty of monitoring, thoughtful design, and robust error handling. They’re tools to augment human abilities, not replace them entirely or function without close monitoring.

Let’s Get to the Real Value

Instead of remaining bogged down in the “agentic” terminology, let’s emphasize the practical benefits these evolving systems have to offer:

• Smarter Automation: Leverage LLMs to automate more responsively and adaptively to natural language input.

• Advanced Workflow Processing: Developing systems that are capable of processing multi-step workflows that previously have required heavy manual coordination.

• Enhanced Problem Solving: Use AI to analyze complex situations, provide solutions, and even perform initial actions. It’s about smart orchestration driven by advanced language models, enabling more flexibility and less direct programming. It’s a shift, a strong one, but maybe not a new generation. The next time you hear someone talking about “Agentic AI” as though it’s beamed in from somewhere outside the universe, remember the foundation upon which it is built.

Let’s be happy to hail the genuine innovations, but keep our feet on the ground, contemplating useful applications and open value, not the buzzword of the moment. The future of AI is already exciting enough without creating new linguistic bottles for old, if improved, wine every other day.