Is the AI Agent Just a Smokescreen? A Software Engineer Exposes the Industry Buzzword Bubble

When Everyone Is Talking About AI Agents, Someone Finally Said the Truth

"There's no AI agent, nor any harness." This is the claim software engineer Fayaz put forward in a technical blog post. He stated bluntly that so-called "AI Agent" and "Agent Harness" are nothing more than invented buzzwords designed to divert attention away from software and software development itself.

This may sound a bit harsh, but Fayaz's logic is actually quite simple: no matter how you package it, there are only two kinds of things that truly exist in the digital world — software, and machine learning models built with software.

Breaking It Down: The Digital World Has Only Two Drawers

Fayaz proposes a classification so simple it borders on brutal: everything in the digital world fits into two drawers.

The first drawer is software. Deterministic programming plus non-tensor data. What is non-tensor data? Control logic, user input, databases, APIs, heuristic rules — all those precisely orchestrated parts that can be fully understood and debugged by human engineers.

The second drawer is machine learning models. That is, model weights obtained through training based on tensor operations. They are not "written" — they are "trained."

Fayaz's core argument is: every digital AI system that exists today falls entirely into one of these two categories, or both simultaneously. There is no mysterious third element.

This is like a software version of Occam's razor — if you can't classify some AI concept into either software or models, then it's probably just a narrative tool rather than a technical entity.

What is a tensor? A tensor is the core data structure in machine learning, best understood as an umbrella term for multi-dimensional arrays: a scalar is a 0-dimensional tensor, a vector is 1-dimensional, a matrix is 2-dimensional, and higher-dimensional data (such as RGB channels of an image or batch processing data) are N-dimensional tensors. The forward propagation and backpropagation of neural networks are essentially a series of additions, multiplications, and nonlinear transformations between tensors. Fayaz uses the boundary between "tensor data" and "non-tensor data" to emphasize: machine learning models process continuous numerical values that can be optimized by gradient descent, while traditional software processes discrete, logical data. The two differ fundamentally in storage format, computation methods, and debugging approaches — the former relies on GPU parallel computing and loss functions, the latter on conditional branches and function calls on the CPU.

Why "Agent" and "Harness" Don't Hold Up to Scrutiny

Using Fayaz's classification framework, let's break down the hottest concepts of the moment.

What is a so-called "AI Agent," really? It's a software program (first category) that calls machine learning models (second category) in a control loop, performs deterministic operations based on model output (API calls, file I/O, database queries), and then feeds the results back to the model. All the orchestration logic, error handling, state management — it's all traditional software engineering.

What is a so-called "Agent Harness," then? Even more straightforward. It's just a software framework responsible for managing model input/output, context windows, and routing of tool calls. To put it plainly, it's a middleware layer. Traditional software engineering is full of such things — call it a framework, a runtime, or an SDK, whatever you like.

Fayaz states clearly in the article: "Everything running on a server (CPU/GPU) is composed entirely of traditional software and tensor-based models. There is nothing else."

This is not a denial of AI's capabilities, but a denial of a narrative — as if there were something within AI systems that transcends software.

What is middleware? Middleware is a software layer between the operating system/underlying services and upper-level applications, responsible for abstracting underlying complexity and providing general-purpose capabilities. Message queues, API gateways, and ORM frameworks are all examples of middleware. Fayaz's analogy of "Agent Harness" to middleware is quite compelling: a Harness is essentially a layer of glue code managing the lifecycle of model calls, responsible for assembling prompts, truncating context, parsing tool-call instructions, and routing returned results back to the model. It doesn't produce intelligence; it only orchestrates. Understanding this is crucial — the middleware layer has historically been a fiercely competitive and easily replaceable space, which precisely shows that "Agent Harness" is not some irreplaceable new species, but a familiar ecological niche in software engineering.

The Economics of Buzzwords: Who Benefits

On the surface, Fayaz's article is a technical classification discussion, but underneath it lies an industry observation.

Why do words like "Agent," "Harness," and "Orchestration" become popular? Because they make something that is essentially software engineering sound like some entirely new species. A new species means new tracks, new fundraising stories, new product categories, new pricing power.

If you admit that "an AI Agent is essentially just a software program with a control loop," then the valuation logic of this field needs to be re-examined. Its barriers to entry, its moats, its competitive landscape — all are closer to traditional software rather than some unprecedented new paradigm.

Fayaz's advice is restrained: "Use these buzzwords when needed, but occasionally remind people what they're actually using underneath." This statement is not a call to arms, but more of a professional habit — a software engineer's instinctive insistence on terminological accuracy.

Why do terms affect valuations? In capital markets, the "story" of a sector largely determines its valuation multiple. Traditional software companies are typically valued at 5-15x ARR (Annual Recurring Revenue), while companies with "AI-native" or "Agent" concepts often command higher multiples because investors expect them to have stronger network effects, higher switching costs, or entirely new technical barriers. When the same thing is renamed from "software service with a loop" to "autonomous agent," the narrative upgrades from "tool" to "labor," and pricing power and TAM (Total Addressable Market) are reimagined accordingly. Fayaz's observation hits the mark: buzzwords are not just rhetoric; they directly participate in value distribution.

Acknowledging Reality Is Not Belittling AI

There is a misunderstanding that needs to be clarified upfront: saying "AI Agents are just software" does not mean saying "AI Agents have no value."

On the contrary, understanding AI systems as software is empowering for developers. Because it means: the software engineering skills you possess — architecture design, testing strategies, error handling, observability, version control — remain valid in the AI era, and are even more critical.

The capability boundaries of the model itself are largely determined by software — what it can do and cannot do. How to manage context? How to route tools? How to recover from failures? How to constrain hallucinations? The answers to these questions are not in the model weights, but in the software architecture.

Fayaz writes in his personal bio that he "firmly believes that even in the AI era, tremendous value can be created through the deliberate implementation of software engineering best practices." This belief is not nostalgia, but a clear-eyed understanding of the essence of technology — models are getting stronger, but what truly makes models valuable is still software.

After the Terminology Bubble Bursts, What Remains Is Still Software

Every wave of technology brings its own buzzwords. Some eventually settle into technical terms with precise meanings; others die away as the wave recedes.

The value of Fayaz's article is not in predicting which words will survive, but in providing a thinking tool: when you hear a new concept, try breaking it down into "software" and "models." If after breaking it down you find nothing left over, then it's probably just a new way of saying something, not a new thing.

This is not cynicism, but basic engineering literacy — figuring out what you're actually working with. After all, when you truly start building AI systems, you'll spend far more time writing software than "designing Agents."

One-line Summary

So-called AI Agents and Harnesses are merely packaged combinations of software and models — there is no mysterious third element.

Paragraph Summary

Software engineer Fayaz puts forward a sharp argument: "AI Agent" and "Agent Harness" are nothing but buzzwords. He argues that everything in the digital world falls into just two categories — deterministic software (control logic, APIs, databases) and machine learning models (tensor operations) — with no mysterious third element. Based on this, so-called Agents are merely software programs with control loops that call models and execute deterministic operations, and so-called Harnesses are merely middleware managing I/O and tool routing. Behind the popularity of buzzwords lies the economic drive of fundraising narratives and pricing power. The article emphasizes that understanding AI as software is not belittling AI, but empowering developers: architecture, testing, observability, and other software engineering skills remain central in the AI era. The author suggests using buzzwords when needed, but reminding people what's actually being used underneath.