Wide River, Shallow Bottom- the weakness of commoditized, generalist AI  

The Pitfalls of a Generic Approach

Solutions like ChatGPT or Copilot are adequate  for general, everyday tasks. They introduce new ways of working: acting more like personal digital assistants than simple tools and help you perform complex intellectual work.

But in many business contexts, commoditized AI performance falls short:

• Superficial answers: Responses can be vague , lack relevant context, or include errors (“hallucinations”) that are hard to detect.
• Incomplete research: Asking digital assistants to extract or analyze specific data (for example, counting the number of  candidates who hold a PhD from a list of CVs) often fails. Even with repeated prompting, the results are rarely complete.
• Human at the end, instead of human in the loop: Deep research agents work much faster than humans, but without the human in the loop. The user is included only at the end, confronted with a complex output. To ensure that the end result is correct requires a full review of the generated text and sources, leading many to correctly ask: if I have to check everything, what is the point?
• Data goes to the AI, not the other way around: Relying exclusively on proprietary, colossal LLMs such as GPT-5, Claude and Gemini requires sending data to their provider. For sensitive or confidential environments, this poses governance or legal risks.
• Sandboxed systems: Commoditized AI solutions often work only  within their own platform (Microsoft, Salesforce, etc.) and struggle to reflect most business realities: we tend to work across multiple tools or even legacy systems
• Vendor lock-in: The convenience of commoditized AI can come at a severe long-term cost if you want to migrate or change providers.

The issue is not the AI model itself. GPT-5, Gemini, or Claude are powerful. The real question is how they are applied. Most off-the-shelf solutions are monolithic, isolated, and designed for breadth, not depth. 

Commoditized AI solutions often integrate poorly with existing systems, and leave little room for human oversight in complex tasks. In short, they are wide rivers with shallow bottoms: visually impressive but fragile where it matters most.

Successful AI Applications are less about technology and more about orchestration

Large AI models like GPT-5 , Gemini, or Claude need careful orchestration to achieve precise, business-specific results. This is where multi-agent systems come in.

Think of it as many AI and human agents, each with a defined role and access to the right digital tools, working together towards a common objective. At Yuma, we call this the Akgent® paradigm.

Each Akgent is autonomous yet part of a modular team: every new functionality is simply “another agent,” be it AI or human, without disrupting the rest of the system. 

There are several key benefits of this approach over commoditized solutions:

• Depth and precision: Each Akgent can focus on a specific, well defined role, delivering more accurate and complete results.
• Flexibility: Akgents can connect to  multiple systems and datasets in an agnostic fashion, adapting to your unique business context instead of requiring you to adapt your business context to a commoditized platform.
• Data ownership and governance: Akgents can live in the cloud or on your local computing infrastructure, ensuring that data stays under your control and reducing compliance and legal risks.
• Human-AI collaborative intelligence: The AI works together with humans in a hybrid team, rather than replacing them. Co-intelligence ensures you can leverage the best of both humans (e.g. accountability, empathy, unique human context)  and AI (speed, access to information).  

Akgents are a true multi-agent paradigm, not a multiple agent paradigm. Real value does not come from the AI model alone. It comes from leveraging human intelligence and AI in ways which work better than the mere sum of the parts. Organizations that benefit from AI the most orchestrate their AI and human Akgents to collaborate and share, while keeping the human oversight where it matters the most.

Akgents® are not a chatbot, they are a vehicle of AI Transformation

Chatbots are an intuitive interface for humans and AI to exchange information and data. But this is where similarities between ChatGPT and Akgents stop. Behind the chatbot lies a dynamic team, custom designed for your needs, integrated with your systems and outfitted with your governance and security rules. 

If your needs are simple, commoditized AI like ChatGPT or Copilot may suffice, but the true value of AI lies in transformation. In order to leverage AI and humans in a way which works better than the mere sum of the parts, you need to rethink your processes, organization and management. You need to shift your focus from POCs and pilots to tangible, measurable results.  Depth, precision, flexibility and control over your data trump convenience of commoditization. 

Akgents are a means to finally achieve such AI transformation.

Enjoyed this insight?
Share it to your network.