Demystifying AI Agents

When ChatGPT was introduced to the masses, it was a turning point for AI, sparking fascination and excitement, but also widespread misconceptions. The public witnessed the magic of ChatGPT’s conversational abilities, and the idea of intelligent “AI agents” soon gained traction. AI startups quickly took advantage of this hype, presenting “AI agents” as groundbreaking, seemingly autonomous entities capable of executing complex tasks. However, many of these so-called agents are essentially wrappers around language models, performing actions that could easily be achieved through basic programming techniques. Behind the complex terminology, AI agents are often no more than a thinly veiled automation layer. In this post, let’s break down the hype around AI agents, how startups are using them to milk out Venture Capital money, shed light on what they actually do, and explore why some of their core functionalities can be implemented with much simpler programming structures.

What AI Agents Are Supposed to Be

At their core, AI agents are software entities designed to interact with their environment, autonomously make decisions, and perform tasks based on user-defined goals. In theory, this requires capabilities such as:

Perceiving the environment through data inputs.
Making decisions based on rational, data-driven calculations.
Acting autonomously to accomplish set goals.
Learning and adapting from past experiences to improve results over time.

For example, in a contact center AI setup, an AI agent should ideally be able to interact with customers, analyze their queries, ask follow-up questions, and autonomously decide whether to escalate to a human agent or resolve the issue. AI agents bring valuable automation to the table by performing tedious tasks, reducing operational costs, and enhancing user experience.

But when we scrutinize some of the so-called AI agents today, especially those built atop Large Language Models (LLMs) like ChatGPT, the complexity starts to unravel. Underneath the elaborate AI descriptions and buzzwords lies an autoregressive model that is essentially a highly sophisticated word predictor. These “AI agents” rely on nothing more than the underlying LLM’s ability to predict words based on a prompt.

The Autoregressive Nature of LLMs: Not True “Intelligence”

To understand the actual limitations of these agents, we need to dive into the mechanics of Large Language Models like ChatGPT. LLMs operate as autoregressive models, meaning they generate responses one token at a time by predicting the next word based on the previous sequence. Their objective is not to think, learn, or adapt—only to guess the most likely next word or phrase given a prompt. The entire system of complex “AI agents” that many companies are claiming to build on top of LLMs is, in essence, just a sequence of prompts and responses designed to create the illusion of autonomous, goal-driven behavior.

How AI Agents Are Just Fancy Function Callers

In many cases, “AI agents” are simply structured around calling different functions based on user input. Let’s break down how this could be achieved without needing a “self-learning” or “intelligent” agent:

Function Selection and Calling: Instead of crafting an agent to “autonomously” perform tasks, we can simply create a list of functions, each with specific tasks or goals. By prompting the LLM with something like:

“Here are my functions: [function list]. Based on this user query, which function is most suitable, and what parameter values should I use?”

The LLM would then return the best match function and parameters in JSON format. With a few if-else statements in a basic programming language like Python, we can trigger the appropriate function with the parameters parsed from the LLM’s response.
Static Parameters and Hardcoded Logic: Even the most complex tasks can often be handled by hardcoded logic. For example, if a user requests the weather forecast, a simple rule might check for specific keywords and call a weather API with preset parameters. The idea of “adaptive, dynamic” agents that companies claim to offer is often exaggerated, as many of these tasks could be resolved by traditional rule-based programming.
Using LLMs to “Suggest” Actions: Much of the “intelligence” attributed to AI agents is nothing more than function suggestion. An LLM can recommend the most appropriate action based on the input, but the actual processing—retrieving or setting the parameters, calling functions, or interpreting the results—requires no real intelligence. For example, an LLM could recommend a support ticket escalation based on keywords indicating user frustration, but this behavior is far from the sophisticated intelligence companies often claim to be building.

AI Agent Architecture vs. Simple Automation

Here are some essential components of an AI agent and how their perceived complexity often differs from reality:

Agent Architecture: Startups describe “agent architecture” as complex and intelligent. In reality, most are simple orchestrations of pre-existing APIs or libraries, wrapped in a conversational layer. This architecture can be physical (such as in robotics) or software-based (a chatbot). But for software agents, the “architecture” is often just an LLM linked to various function calls.
Agent Function and Program: The supposed decision-making algorithms that AI agents use are generally simplistic, with rules or prompts guiding function selection. The actual “program” part, which is the code implementing tasks, is usually straightforward function-calling logic that hardly constitutes “intelligent behavior.”
Feedback Loops and Learning: Many agents claim to learn over time, but true learning requires reinforcement mechanisms and adaptation algorithms far more advanced than what most “AI agents” have. In many cases, agents are simply fine-tuned based on usage data or re-prompted by developers rather than independently learning from their environment.

So, Why All the Hype?

Venture Capital funding often drives this hype. Many investors are not experts in ML or LLMs, so the allure of “autonomous AI” and “self-learning agents” becomes a powerful marketing tool. Startups cleverly leverage complex-sounding jargon, making it appear as if these agents have advanced cognitive capabilities, when in reality, the technology falls short.

The tendency to view LLMs like ChatGPT as “intelligent” likely comes from their conversational abilities, which can mimic human-like interactions. But interpreting words contextually is not the same as “understanding.” At its core, an LLM-based agent is just a predictive model that follows set instructions based on learned patterns.

What’s the Real Value of AI Agents?

In fairness, AI agents—despite their inflated descriptions—do bring some real benefits:

Automating Repetitive Tasks: They’re useful for offloading tedious tasks like data entry, ticket generation, or basic Q&A.
Improving User Experience: As sophisticated question-answering systems, LLM-based agents can handle customer support, offering responses that seem “intelligent” and personal.
Streamlining Decision-Making: By providing information or making recommendations, these agents can aid in quick, data-backed decisions, even if the “decisions” themselves are based on pre-structured patterns.

Beware of the Hype, Understand the Limitations

AI agents, especially those built on top of LLMs, are far from the autonomous, intelligent systems many startups claim them to be. At their heart, they’re predictive text models acting on prompts to call pre-defined functions and, in some cases, route through different APIs. The most sophisticated use cases rely on nothing more than complex prompting and basic programming logic, which could be achieved through simpler means.

While AI agents can undoubtedly save time and automate tasks, it’s crucial for both users and investors to understand what these systems can and cannot do. We should view AI agents for what they truly are: enhanced automation tools built on LLMs, designed to make pre-programmed decisions—not the all-powerful autonomous entities that the marketing hype suggests.