The Hype and Reality of AI Agents: Are We There Yet?

AI agents are everywhere, at least in the news and media—

If you listen to the hype, they’re about to take over customer service, plan your vacations, and handle every little life admin task you hate. But if you've actually tried using one, you might have noticed that reality isn't quite there yet.

Over the past few months, I’ve been diving deep into the world of AI—more than just using ChatGPT for quick answers or playing around with AI-generated images. I’m currently working my way through an eight-week course on LLM engineering, while simultaneously improving my programming skills in Python, HTML, CSS, and JavaScript. When I first started, I expected AI agents to be far more advanced than they actually are. The way the media presents them, you’d think we’re just a few months away from having fully autonomous digital assistants handling complex tasks without human intervention. But as I dug deeper—reading research papers, testing AI models, and even building small-scale agents myself—I realized that the reality is much more nuanced.

There’s no doubt that AI agents are evolving rapidly, but they’re nowhere near the sci-fi level of intelligence some headlines suggest. Instead, most current AI agents are really just sophisticated automated workflows—chaining together multiple LLM calls, using APIs, and performing predefined steps. True agency, where an AI can independently strategize and execute tasks, is still in the early stages of development.

At the same time, I see the potential. The building blocks are there. As someone who is learning how these models function, I’m gaining a clearer picture of where AI agents are headed and what they need to actually work at scale. In this piece, I’ll explore the real progress being made in AI agent development, the limitations that still exist, and the steps needed to move beyond basic automation.

But what Even Is an AI Agent?

The term “AI agent” gets thrown around a lot, but what does it really mean? Back in the early days of computing, automation meant rigid, rule-based scripts. If you needed to extract data from a website, you wrote a scraper that followed a strict set of instructions. Now, with AI models like Claude and ChatGPT, we have systems that can adjust their behavior dynamically.

Unlike traditional workflows—where step A leads to step B and then to step C—an AI agent can decide on the fly how many steps are needed. Think of a customer support chatbot: a simple version just answers pre-programmed questions, but an AI agent might ask clarifying questions, search for solutions, and refine its response based on your reaction.

The distinction between traditional workflow automation with LLM integration and fully autonomous AI agents lies in the level of adaptability and control. Traditional workflow automation is structured, relying on predefined steps and rule-based decision-making. LLMs in these systems enhance efficiency by handling natural language processing tasks, such as summarization or categorization, but they still operate within rigid frameworks.

In contrast, fully autonomous AI agents are dynamic, capable of deciding their next steps based on real-time data and learned experiences. Instead of merely responding within pre-scripted paths, these agents iterate through tasks, adjust strategies, and improve autonomously over time. This adaptability means they can identify new solutions, interact with various tools, and handle unexpected situations without human intervention.

A key enabler of AI agents' increasing autonomy is their ability to interact with external tools. Tools significantly extend what an AI can accomplish beyond mere text generation. One clear example of this is Retrieval-Augmented Generation (RAG) pipelines. These pipelines allow AI systems to access and retrieve external information that is not contained within the LLM's static memory, thereby improving response accuracy and contextual relevance.

Consider a chatbot designed to help users book flights. The underlying AI model is given a predefined role such as: {"Role":"System","Content":"You are an airline assistant that helps users to book flight tickets"}. While this system can provide guidance, it is not inherently capable of fetching flight prices. However, by integrating function-calling capabilities, the AI can recognize when a user asks for a flight price, for example to Paris and activate a specific tool to retrieve real-time ticket prices. Once retrieved, the system presents the user with options and allows them to make a purchase. For this example I quickly made an airline assistant, and also showcasing the option to generate images autonomously.

This example somewhat highlights why the future of AI agents is not solely dependent on better models but rather on the seamless integration of tools. The more tools an AI can access, the greater its ability to function autonomously. While AI agents already appear magical to many, their true power will emerge when they can effectively leverage an ecosystem of tools to execute complex, multi-step tasks beyond simple conversation.

For people interesting in finding tools to automate their workflow look at the following:

• Make.com

• Zapier

• N8N

• LangChain

The Quiet Wins: Where AI Agents Are Truly Making an Impact

So, if AI agents aren’t yet planning our dream vacations, where are they making a real difference? The most impactful uses of AI are often the ones that streamline small tasks, saving valuable time in ways that scale. One area where this is particularly evident is the recruiting industry, where AI is frequently used to efficiently screen large volumes of applicants.

To illustrate just how simple it can be to automate these processes, I tested one of n8n’s templates. This workflow allows you to retrieve data from a form submission, automatically upload the applicant's CV to Google Drive, and then screen applicants based on predefined rules.

In the workflow shown above, the system automatically extracts relevant qualifications and personal information, merges them, and sends them to an AI model like GPT-4 Mini. From there, you can either manually review applicants with a quick summary or allow the AI to vote on which candidates to prioritize, narrowing down your applicant pool. Finally, the AI outputs a score into a Google Sheet for easy tracking.

Similarly, AI is quietly improving code generation. Automated debugging and AI-assisted coding tools are cutting down on developer grunt work, much like spellcheck revolutionized writing in the 1990s. These aren’t headline-grabbing advances, but they’re making a real impact.

What’s Next for AI Agents?

By 2025, businesses will likely be using AI agents extensively to automate boring, repetitive tasks. But for personal use? We’re not there yet.

One interesting possibility is multi-agent systems—where multiple AI agents work together. Imagine one AI handling flight searches, another picking hotels, and a third optimizing your itinerary. That could make AI-powered vacation planning far more practical.

But before that happens, AI needs to get better at error-checking and verification. This is something we've seen before—think back to early search engines, which often returned unreliable results before algorithms improved. Until AI agents become more trustworthy, they’ll remain useful tools but not full replacements for human judgment.

Final Thoughts: Don’t Believe the Hype—Yet

If you’re considering using AI agents in your work, my advice is simple:

• Use them for small, well-defined tasks where mistakes aren’t costly.

• Always verify their output—we’re not at full autonomy yet.

• Think of them as assistants, not replacements—the best AI setups still involve human oversight.

The bottom line? AI agents have potential, but they’re not quite the game-changers they’re made out to be.

The history of technology has always been about balancing excitement with realism. Just as we saw with the internet, smartphones, and even the rise of personal computers, the real breakthroughs come not when tech tries to replace humans, but when it enhances what we already do. AI agents may not book your dream vacation flawlessly yet, but they’re already reshaping the way we work—and that’s worth paying attention to.

What’s been your experience? Have AI agents helped you, or are they still more hassle than they’re worth? Let’s discuss!