What do we mean by AI automation, actually?

One of the biggest misconceptions we observe at Vstorm AI Engineering Consultancy is in the very core of client expectation. The ‘AI Agent’ is often perceived as a replacement of a+ given role, one that “just needs to be put in,” but this is far from the case. Allow me to explain.

With the expansion of LLM (Large Language Model) capabilities, these models started to be evaluated against each other to show how one is better than the other in ‘reasoning’ or ‘simulated jobs,’ but never truly tested against real work.

In October of 2025, OpenAI released a framework to evaluate AI model performance based on Real-World Economically Valuable Tasks, which has been used by researchers to display that automation measured in this method consistently fails. And the rate of failure is obscene: reaching 96,25% failure rates across a wide variety of jobs.

In other words, inserting AI into a human role or position shows that AI achieves basic human performance in only 3,75% of cases. This is a staggering difference to what we observe in our project deliveries, where our AI agents out-perform human effort in their applied roles in ~95% of cases.

So what creates the difference? LLM evaluation versus Agentic AI capabilities

The difference arises from our expectation of AI systems. The researchers in the studies cited above generally test publically available models, such as Opus from Claude, Gemini from Google, or ChatGPT from OpenAI, in job roles with the intent to verified if out-of-the-box models could perform better or equally well as people would.

Because that is what AI automation is meant to be about, right? But this does not reflect the technologies applied reality, with the 3,75% success ratio standing as proof that this manner of thinking is fundamentally flawed.

AI automation which successfully and consistently yields high results (in the range of 90%+ success rates) is very different. They are typically:

• a meticulously designed system, engineered to leverage general LLM capabilities
• operating on a given company’s proprietary data, not on pretrained data freely found online
• have been extensively validated in the quality of their provided outputs before implementation

These three things make all the difference and form the ocean-wide gap in between successful implementations and failures.

Autonomous AI Agent applications

Let us take a closer look at exactly what I mean by this. Below you will find a breakdown of three applied Agentic AI automation cases where the provided solution dynamically alters the company’s workflow, not replacing human led jobs, but providing direct value in spaces human beings cannot fill.

Agentic AI establishing complex custom workflows: automating automation

Synera is a company which operates an AI agent platform designed to enhance engineering projects, integrating popular CAD, CAE and PLM software. Their agents and automations have helped accelerate the product development process by up to 10 times, mostly with the reduction of workflow complexity and automation.

We at Vstorm built a text-to-workflow system for Synera and their AI Agent platform using LLMs, RAG, and validators. The system operates using graphical nodes inside of Synera’s visual engineering-automation platform.

And while this solution enables users to easily create their own custom automations, it took time and a deep understanding of the platform’s capabilities to build more complex workflows, creating barriers for new users and demanding training time that could have be spent in a other ways, for example, engaging in product design.

Synera, following their company’s core value to help their customers shine, decided to solve this hurdle for users by introducing our AI-powered automation agent to their platform.

The agent turns each prompt into a workflow automation which fits to the user’s intent, which can then be fine-tuned and adjusted to their explicit needs, not only liberating hours of work, but also encouraging engineers to tinker around and test if some other aspects of their work can be automated or streamlined.

You can catch up on all the details of this implementation in our text-to-workflow case study, but here are the take-aways at a glance:

• 2 minutes to generate a full workflow from a written prompt
• 0 hallucinations with multi-layer code inspections
• 1 hour 58 minutes saved on average with every workflow creation

This represents real value augmenting a workflow with efficiency that human effort simply could not replicate.

Agentic AI offering customers expert advice in print-on-demand

Mixam is a self-publishing company providing printing and fulfillment services for independent authors, publishers, and creators on a global scale. They specialize in high-quality print production, including books, magazines, and other printed materials.

Our provided AI agent was designed and implemented to help Mixam’s customers navigate the company’s complex printing offers, centered around creating a satisfying experience for new users who were just starting their self-publishing journey and beginning to explore the range of options available to them.

It was all too easy for any non-publishing professional to get lost in the variety of choices for their first publication to be created the way they envisioned it. Our Agent now helps customers get exactly what they desire from Mixam’s expansive offer.

You can read about the Multi-agent system we created for Mixam here, but here are the take-aways in TL;DR:

• 70% of new users provided with expert guidance
• 11.76% increase in orders from day 1
• 95.4% success rate in workflow results
• 62.11% of all quotes provided by the Agent paid

This agent provides unprecedented scalability for a business which had dynamic potential for growth but no way to actualize it through human effort.

Multi-channel AI Agent facilitating personalized appointments in Healthcare

A US-based healthcare company contacted us with a mission to provide high-quality, affordable, and easy-to-understand healthcare plans for seniors. The company specializes in Medicare Advantage offerings and leverages advanced technology to enhance healthcare delivery. Operating across multiple locals in the United States, this organization serves over 100,000 members.

We deployed an AI Agent dedicated to the pre-appointment phase, allowing patients to share critical updates and concerns well before seeing their doctor, ensuring the solution met strict healthcare compliance requirements while delighting both patients and care teams.

In practice, our AI Agent analyzes patients’ entire record; including medical history, physician notes, active health issues, visit history, personal details, and current medications; while also gathering any additionally required information directly from the patient using multi-channel methods tailored to senior patience. At the same time, the solution continuously builds a robust data foundation for future use, keeping in compliance with HIPAA standards.

You may read more details on the agent and its operation in this Multi-channel AI agent case study, but here are the take-aways in short:

• Saves 5 working hours per week per doctor
• 20% rise in patient engagement

Considering the high value of the time of healthcare professionals, this automation effectively frees hundreds of hours of critical human effort per month.

Clearing up the misconceptions

We advise not buying into the narrative of a general LLM may miraculously become “smart enough” to fill in a role all on its own. It is not that smart, nor that informed, and it may never be, given that companies protect their ways, methods and data to keep their competitive advantages.

If such an AI does come to exist, it will not be given out easily, else other models can be trained on it, as this could put the creators out of business. Business owners and decision-makers know that. Revolutionary new technologies are nothing new under the sun.

Remember when in Top Gun the commander suggested that Maverick and his piloting skills are obsolete, as drones will eventually take over? His reply was, “Maybe so, but not today.” The same applies to AI agents.

Instead, a smart way to approach agentic AI is to see it more as an advanced tool that leverages LLM capabilities in a particular, tightly controlled, and well-defined way to facilitate meaningful business transformations, not replacing human roles, but enhancing them. And it truly can, if you know how to deploy it.