By Varun Choudhary Co-Founder & Chief Executive Officer at ORO
AI agents have grown astronomically in recent years, with some forecasts predicting they’ll cross 2.2 billion by 2030. But most of these agents are AI wrappers, with structurally identical capabilities. In a highly competitive market, AI agents won’t have an edge unless they provide proprietary solutions.
Instead, AI products with specialized models, unique domain knowledge, multi-step pipelines, and feedback-based evaluation frameworks are the pillars of defensibility. Users will spend their time and resources on AI products that solve real-world problems, rather than managing hundreds of AI wrapper-based agents.
AI wrappers are intrinsically fragile
Most AI agents are wrappers with a unique UI/UX and an API call to a foundation model. So, the agent’s value proposition depends on the underlying model’s capabilities.
If AI agent developers replace a GPT model with Claude and generate the same results, that’s even worse. Model agnosticism means the particular AI agent doesn’t have a proprietary market offering.
However, most of these agentic tools create an illusion of easy access without necessarily improving business outcomes. These AI wrappers consume input tokens and may fluently respond to customer queries.
But wrappers fail to have a lasting impact on KPIs like resolution rates and cost per ticket. AI wrappers also perform poorly because the system doesn’t improve over time through internal data feedback.
Since it’s easier to build AI wrappers, developers sometimes create products with identical use cases, thereby fragmenting the agentic market. Consequently, user acquisition costs shoot up, accompanied by pricing challenges as hundreds of wrapped agents compete for the same users.
Enterprise clients also refuse to invest capital for a wrapped AI agent that their teams can build or easily replicate. Eventually, sales stagnate, and clients rely on their homegrown products, with wrappers sending queries to AI models for curated responses.
Moreover, a wrapped AI agent becomes useless once the underlying model providers natively add the same functionalities or significantly improve their capabilities. A user can directly access ChatGPT or its equivalent, provide the context, and generate a similar output.
With no growth in the agent’s characteristics or features, the wrapper inevitably loses value in the long term. That doesn’t mean wrapper-based AI agents are doomed to failure. They can initially get users, generate revenue, and have high retention rates if the UI/UX is better than the underlying model’s interface.
But these AI agents have an infrastructural limitation that AI products don’t have. No wonder that users have started focusing on AI products that compound their performance scores over time.
The defensibility of AI products
Most AI agents improve only if the underlying AI models improve, thereby negating any competitive edge of a particular agent. But there are AI products whose cornerstones are AI models, making them stand out in a crowded market.
One of the most important differentiators of AI products is their proprietary domain knowledge. The AI products whose models are trained on exclusive business data or user-generated queries are impossible to replicate. Competitors don’t have access to an AI product’s data moats and thus remain limited in scope.
AI products that leverage specialized domain-specific data and accumulated expertise get a structural advantage. These products can tap into user interactions and edge cases that entrench product accuracy and compound performance over time.
Each output rating, request for correction, and behavioral sign directly contributes to product improvement. Whereas wrapper-based AI agents can’t avail themselves of the same benefits because they’re limited to providing UI/UX without any control over feedback data.
AI products become more efficient as they scale operations, with model fine-tuning as a crucial aspect of product development. Besides model tuning, RAG enhances product performance by understanding information structure and handling multiple sources to retrieve relevant content.
When an AI product has its own proprietary datasets and a rich internal knowledge corpus, RAG systems have an edge over wrappers. That’s because AI wrappers simply have access to the model API but not to the information base.
Unlike generic wrapped agents, AI products deal with complex multi-step workflows that include error handling and decision logic design. Such pipelines refine AI products, as they get better through learning patterns and specific, internal knowledge.
Therefore, the message is clear: AI wrappers can package capabilities into user-friendly interfaces, but they’ll always remain dependent on the underlying AI models. So, wrapped AI agents will be accessible and adoptable in the short term, but difficult to improve separately from AI models.
AI wrapper-based agents’ destiny lies in their death as interfaces, not as transformative systems. Ultimately, it’ll be AI products that control business outcomes, while wrapper agents remain limited to mere interaction points. And that’s why users will spend time on AI products and not worry about managing agents.
