Off-the-shelf AI platform or Custom AI Agent solution?

At Vstorm, we’ve noticed a new trend in the AI world: Companies that initially jumped on off-the-shelf Agentic AI platforms (like Botpress) are now turning to custom-built solutions. This move isn’t just a random pivot; it’s the natural evolution of businesses that demand tangible, trustworthy outcomes. While platforms like Rivet, Botpress, Vellum, EngAIge, and dataRobot enable the rapid development of AI agents, they often fall short of meeting more complex and critical business requirements. And in a world where your AI agent might be the face of your brand, “close enough” simply doesn’t cut it.

What is an AI Agent?

An AI Agent is an intelligent system powered by a Large Language Models (e.g., GPT-4o) capable of making decisions, reasoning through problems, and taking relevant actions without human intervention. For example, imagine a customer service AI that independently decides to review product documentation, check internal databases, and generate a personalized response based on a customer’s order history. It’s not just a chatbot — it’s an autonomous decision-maker representing your company around the clock.

Off-the-shelf AI solutions

Agentic AI SaaS platforms promise a quick start. With pre-configured workflows, no-code interfaces, and easy integrations, these tools help businesses deploy AI without hiring an army of developers. For straightforward tasks, that’s great. But many organizations discover that these platforms become restrictive the moment they need:

• Customization that aligns perfectly with unique business processes.
• Advanced reliability to avoid humiliating AI “hallucinations” that erode trust.
• Control over data to ensure security and compliance across different regions.
• Scalability to continuously refine and grow alongside evolving business needs.

If any of these are high priorities for you, off-the-shelf can quickly turn into a bottleneck.

I don’t want my AI agent, acting in the company’s name, to hallucinate

Let’s start with the basics – what exactly is hallucination in the context of AI Agents?

For technical people, it’s nothing more than the generation of erroneous information that is not supported by reality or the data provided. For business owners and executives, it’s something much more serious – a potential threat to the reputation, credibility and security of the business.

While we might chuckle at a chatbot’s absurd restaurant recommendations, hallucinations become a critical business liability when AI represents your brand. Our customers understand this risk intimately — they demand reliability as the non-negotiable foundation of any AI deployment. When an AI agent speaks for your organization, it must:

• Deliver accurate, verifiable information.
• Leverage appropriate tools to retrieve relevant data.
• Maintain strict contextual boundaries through robust guardrails.
• Generate responses that align with business objectives and brand voice.

Imagine a customer support agent hallucinating about your return policy or a financial advisor fabricating investment performance data. These aren’t minor inconveniences — they’re crucial events that erode trust. The reality is that off-the-shelf AI platforms simply lack the sophisticated customization capabilities needed to mitigate these risks effectively. Their one-size-fits-all approach leaves dangerous gaps in reliability precisely where businesses can least afford them.

How do we make agents adhere to business and not drift off?

Here’s where ready-made platforms diverge from what is possible with custom AI agent development. AI development enhances task execution by equipping a custom AI agent with the necessary design patterns and capabilities. At Vstorm, we enjoy full freedom in planning and developing an approach that is reliable and meets customers’ needs. What we implement differs from case to case, yet our toolbox of tricks consists of:

• Advanced Guardrails: We deploy lightweight, domain-specific models (like Prompt-Guard-86M) that act as the first line of defense, identifying off-topic queries, potential jailbreak attempts, or questions outside the agent’s knowledge boundaries before they reach the main LLM.
• Enhanced RAG: We employ state-of-the-art methods (e.g., hybrid search, cross encoders) to refine and prioritize results based on contextual accuracy. For complex documents, custom advanced OCR systems are utilized to parse and extract structured data from intricate tables. Together, these steps mitigate hallucinations.
• Human in the Loop: Custom systems can score the AI’s certainty on each response. If it’s below, say, 80% confident, the query can be escalated to a human. Off-the-shelf platforms provide some generic metrics, but they’re not customizable or actionable in the same integrated way.
• Feedback Loops: We put in elements to capture user interactions and outcomes in order to continuously refine agent performance by fine-tuning models and identifying shortcomings.

The critical limitation of off-the-shelf platforms is their inability to implement and combine these sophisticated reliability mechanisms. They typically offer only basic guardrails and limited integration options, leaving businesses vulnerable to hallucinations precisely where accuracy matters most. Furthermore, these platforms rarely provide the necessary transparency into how responses are generated, making it impossible to systematically identify and address reliability issues.

Where have custom agentic AI workflows replaced standardized platforms?

In our projects, we have already managed to replace out-of-the-box systems for processes such as:

• Information summarization for media outlets — mixing facts with fiction resulting from hallucination would defeat the purpose. In these projects, intelligent agents are utilized to enhance automation in decision-making and problem-solving tasks.
• Support center agents — Since unreliable answers related to customer orders are a show-stopper, we designed and implemented solutions that do not let that happen. Those systems do work in combination with a variety of other systems they pull information from (on order statuses, knowledge bases, and such), making them a crucial contributor to customer’s experience. 
• Information collection & ranking  — thanks to scalability beyond what human teams can do, agent-based systems can work great to automatically scrap information (such as articles or posts), rate them, and provide an automated ranking of authors or information sources. The media houses we work with all replaced standardized platforms for the custom systems we’ve built for them. 

Schedule a call

Talk to a knowledgeable expert, not a chatbot

Bart, PhD economist and our co-founder, is ready to leverage his hands-on experience of:

• Entrepreneurial, and C-level roles
• Exited and supported startups
• Executive Consulting background

to discuss your project on a 20-minute introductory call.

Dr. Bart Gonczarek

Vice President

Making the right choice

Right now, you’re standing at a crossroads, choosing between two distinct paths.

Off-the-shelf AI solutions exist for a reason. They’re easily accessible, quick to implement, and sufficient for simple tasks. That’s why the market is full of competing platforms, each fighting for your attention. But there’s a catch—they all have limits. Sooner or later, the road gets bumpy, the challenges become more complex, and the cracks in these solutions start to show.

Then, there’s the second path—a custom AI agent. At first glance, it may seem like a bigger challenge, requiring more effort upfront. But when looking at the long journey ahead, it’s the smarter choice. The very issues that off-the-shelf solutions struggle with become minor hurdles—easily solved, allowing you to move forward without friction.

Think of it this way: off-the-shelf AI is like a quick snack. It gives you a temporary energy boost, but soon enough, your energy crashes, leaving you worse off than when you started. Meanwhile, a custom solution is a full-course meal—something that truly satisfies, sustains you, and provides the fuel you need for the long run.

We hope this article has helped cut through the marketing noise and shed light on the real picture—so you can make the right choice for your business.