Why Agentic AI is the Future of AI

Authors: Brent Collins and Lynn Comp

The Evolution of AI: Embracing Federated Systems for a New Era of Intelligence 
 

The rapid advancement of AI has captured the world’s attention and accelerated the rate of innovation across industries. Its capabilities are remarkable: different AI models can draft business proposals, solve complex math equations and automate customer service. But one limitation of AI so far is that no one model can do it all. Taking AI to the next level requires a federated approach that combines the strengths of various AI types with more traditional logic programs. 
 
Take large language models (LLMs). Whether they’re writing a novel or just having a normal conversation with a user, their human-like creativity is impressive. Yet, upon deeper inspection, it becomes clear that these models only mimic intelligence. They are like a student reciting information rather than truly understanding the material. Injecting seemingly related yet irrelevant data or altering names and numbers can affect response accuracy. In contrast, predictive AI and logic programs can easily and accurately handle mathematical calculations — but they lack outward creativity. 

Moving toward a federated AI future 

To address these limitations, we should embrace a federated approach that combines the strengths of various AI types and more traditional logic programs. Generative AI (GenAI) can serve as a general-purpose orchestrator between humans and underlying distinctive AI models, interpreting and organizing requests and then employing the specialized capabilities of other models to complete tasks accurately and cost-efficiently.  
 
This approach is beginning to take shape with agentic AI, an evolving technology that can perform complex tasks based on a variety of data and inputs and learn from its experiences to inform future actions. Agentic AI achieves this by leveraging affiliated specialized AI models — each contributing its unique strengths to achieve a coordinated result accurately and cost-effectively.  

The level of “agentic-ness,” expressed as the degree to which a system can autonomously and adaptably achieve complex goals in complex environments with limited direct supervision, is on a wide spectrum and determines the type of agentic AI system. As this approach matures, there will ultimately be a path for these kinds of agents to operate fully autonomously. 

Imagine a world where the creative insights of GenAI are seamlessly integrated with the precision of traditional logic programs and the adaptable learning of applied AI. This will usher in an era of unparalleled efficiency and innovation, transforming how we approach problem-solving and decision-making in the tech industry. 
 
A critical element of agentic AI is the ability to share data across different AI instances. Data is the foundation upon which AI is built, so the better the data that feeds the system, the better the results will be. In an agentic AI model, all relevant data must be federated across generative, predictive and logic-based programs to ensure accurate and contextual results. This is especially important in complex tasks that require a combination of creative and logical reasoning. Sharing data also reduces bias errors caused by incomplete data.  
 
Federated data is dependent on an advanced data framework that enables continuous — and near-instantaneous — two-way exchanges of data between models. Like the way information flows in human-based organizations, data must be properly updated, synced and governed to ensure it is protected from malicious threats. Security solutions like Intel® Software Guard Extensions (Intel® SGX) and Intel® Trust Domain Extensions (Intel® TDX) can also help protect confidentiality. 

Real-world agentic AI  

The ability to perform specific tasks or manage processes autonomously unlocks numerous real-world applications. AI agents can interact with users, gather necessary information and make decisions or recommendations without the need for constant human oversight. From coordinating healthcare appointments and diagnoses to managing personal finances and safeguarding digital security, agentic AI is revolutionizing industries by streamlining operations, enhancing personalization and improving efficiency. Consider the following examples: 

• Before your next doctor’s appointment, you might get an email from the clinic asking you to log in to interface with its agentic AI agent, which would then coordinate your care. It would verify clerical details and medical history, as well as intake required consent forms and insurance information. From there, it could even begin a diagnosis through chats, images, videos and/or biometric data, and ultimately recommend treatment. 
   

• In the financial services industry, agentic AI could bring significant capabilities to light for holistic financial planning. Agents could collect your financial information and develop a comprehensive and personalized financial plan, complete with budgeting, investment portfolio development, tax minimization and retirement drawdown strategies.  
   

• In a Security Information and Event Management (SIEM) framework, agentic AI could help detect, analyze and respond to security threats autonomously and in real time. It would save only the most difficult, complex or sensitive tasks for human intervention.  

The path forward 
 

As we move along the path toward federated AI systems, it’s important to proactively collaborate across various AI disciplines. By integrating the best of GenAI, traditional AI and logic-based programs, we can create powerful and efficient capabilities that leverage the strengths of each approach.  

This paradigm shift of federated AI not only enhances our technical capabilities but also continues to push the boundaries of what AI can achieve.  

At Intel, we are continuing to develop our AI capabilities, building on a foundation of innovation started in 1986. To explore existing AI solution capabilities, please visit the Intel AI Software catalog. 

Read more: Agentic AI and Confidential Computing: A Perfect Synergy for Secure Innovation