As artificial intelligence (AI) continues to evolve, the industry is witnessing a growing trend towards more specialized and modular systems. One of the most promising developments in this area is Agentic AI—a system design that utilizes multiple AI agents with a focused, distinct role. This architecture breaks down complex tasks into smaller, manageable components, with each agent contributing its specialized expertise to the overall outcome.
While GenAI is often seen as a powerful solution, Agentic AI appears to offer an even more refined approach. However, evaluating both the benefits and potential limitations is important when making decisions about products, features, or system architectures.
The Advantages of Agentic AI
Agnetic AI has a strong logical structure and ideas. One of its key strengths is the separation of concerns, a classic architectural principle. This approach breaks a large system into smaller, specialized components. Each component is fine-tuned, optimized, and trained on specific data to deliver focused content, code, or insights.
This “divide and conquer” method is proven. In enterprise architecture, we’ve seen this concept evolve over the years. Microservices, which replaced monolithic systems, brought benefits like scalability, better isolation, and higher quality in individual components. This model improves testing and results in more specialized, higher-quality output.
However, human decision-making often involves intangible elements, like emotional interpretation or a deep understanding of enterprise risk management (ERM) and risk appetite. People bring empathy and experience to decisions, which can make the difference between a good idea and a successful product. With AI, including Agnetic, decisions are based on logic and quantifiable risks. While this can generate valid results, it lacks the human element.
AI may overlook key ERM parameters or guardrails, and since there’s no human validation, the outcome may miss critical context. This is a fundamental gap in machine-generated content or decisions.
The Challenges of Agentic AI
One of the main downsides of using multiple AI agents is the inherent need for determinism in generative AI. When you submit a prompt, the model responds. However, if you ask the same question again, the response may differ—sometimes factually the same, sometimes not, but framed or expressed differently. This variability can lead to hallucinations, biases, and factual errors.
Even with a well-trained, highly accurate model that delivers correct responses 99% of the time, adding more agents increases the risk of mistakes. Each agent will generate its content, code, or solutions, and because generative AI is inherently non-deterministic, the likelihood of errors compounds. The result may be lower overall accuracy, as mistakes or biases in one model’s output can influence the next in a chain, leading to even more significant errors.
For instance, if a series of models is working sequentially—where each model builds on the output of the previous one—an initial error can snowball. A bias or hallucination from the first model can degrade the quality of the final production. This creates a complex challenge: troubleshooting becomes increasingly difficult as the system becomes more intricate. Correcting errors isn’t straightforward because the AI’s behavior is not consistent.
From Data to Action: The Next Step in AI Evolution
Until now, much of the focus has been on presenting data—using RAG, chatbots, and data mining to retrieve and display information, whether for humans or other systems. The goal has primarily been reading data and generating content for further use. But there’s a limit to what can be achieved by simply reading information; sometimes, action is required.
This brings us to actionable AI. The next big challenge for AI is moving beyond just representing data to performing actions. Think about scenarios where a traveler changes a ticket, someone makes a purchase, or a patient schedules a medical appointment. These are sensitive tasks where mistakes can have real consequences, and the AI handling them must be accurate—there’s no room for hallucinations. In healthcare, for instance, a patient’s well-being may depend on whether an appointment is scheduled correctly.
Transitioning from conversational AI and chatbots to systems that can execute tasks autonomously is a significant step forward. We’re already seeing AI generate code and solve problems, but the next step is creating an engine that can act on that code, like interacting with APIs in real-time, as if it were a human performing the task.
Lastly, smaller models for specific tasks offer clear advantages—higher accuracy, better precision, enhanced data privacy, and more. This makes agent-based AI highly valuable.
How DataArt is Leading the Way
At DataArt, we are at the forefront of helping businesses navigate the complexities of AI implementation, including adopting Agentic AI. Our expertise lies in creating AI platforms that combine the strengths of multi-agent systems with high-quality data integration, ensuring that AI solutions are both scalable and reliable.
Our AI consulting service is designed to help businesses understand the complexities of AI systems, assess their needs, and implement solutions that balance innovation with risk management. We also emphasize integrating AI with robust data infrastructures, ensuring that models are trained on accurate, relevant data from various departments and systems.
By providing tailored AI platforms that connect data and AI models, DataArt helps businesses unlock new opportunities, enabling them to stay competitive in an ever-evolving market. Whether through multi-agent systems or broader AI solutions, we are committed to helping our clients meet the growing demands of today’s industries while mitigating risks and maximizing value.
