Agentic AI vs Traditional AI: What Sets AI Agents Apart

As AI continues to transform the business landscape, another promising subset is poised to disrupt the status quo: agentic AI. Gartner predicts that by 2028, 33% of enterprise software applications will include these next-gen AI models, up from less than 1% in 2024.

This trend indicates a significant shift in how businesses use and integrate AI. IBM identifies many examples of traditional AI systems, such as Google’s search algorithm, recommendation engines, and voice assistants, as early-stage examples of agentic AI. However, while useful, these systems are limited by their data and predefined rules. Agentic AI takes their systems further, combining modern innovations with the accuracy of traditional programming to achieve their characteristic agency.

With more companies looking to automate their workflows, the distinction between agentic vs traditional AI systems is becoming increasingly important. Understanding what sets agentic AI apart– and why that difference matters– is vital for organizations aiming to stay competitive in their market. 

What is Agentic AI?

Agentic AI refers to AI systems and models that process information and act autonomously to reach set goals. This goal-oriented approach is sometimes referred to as “human-like reasoning.” 

These next-gen AI models use LLMs that draw from multiple sources and external tools, which allow them to solve complex problems, adapt to data to improve over time, and handle challenging multi-step tasks. 

For example, autonomous AI systems could proactively reconfigure supply chains in response to sudden storms, economic fluctuations, or other disruptions. As supply chain disruptions cost the average organization 45% of one year’s profits over a decade, this dynamic approach could help businesses in the logistics industry save time, money, and resources.

“You can define agentic AI with one word: proactiveness,” says Enver Cetin, an AI expert at the global Experience Engineering firm. “The agentic AI system understands what the goal or vision of the user is and the context of the problem they are trying to solve.” 

What Are the Benefits of Agentic AI vs Traditional AI?

Agentic AI takes much of traditional AI’s groundwork and expands upon it. Where traditional AI (sometimes called narrow AI models) requires user input and predefined workflows, AI decision-making systems can operate independently. They review options, make choices, and execute actions to reach their goals.

Additionally, while traditional AI models are effective at handling specific tasks, they struggle to generalize from training data and adapt to new situations. Next-gen AI models, meanwhile, actively recognize, memorize, and respond to changes in their surroundings. Moreover, they can self-reinforce to learn and adjust their behavior over time. 

AI agents are also capable of collaboration, allowing agents specialized in different tasks to face challenges as a team. These qualities make agentic AI highly dynamic and capable of automating complicated tasks. Because agents can cross-collaborate, they’re better suited to solving issues with minimal supervision. 

Siri and Alexa, for instance, utilize a traditional AI model. They have been trained to do a specific job and, while effective, are limited to their preprogrammed instructions. Apple also required human intervention to expand Siri’s knowledge base and functionality. 

In contrast, SciAgents, a team of multiple AI agents developed by MIT researchers, autonomously identified and suggested a biomaterial that combines silk with dandelion-based pigments for a stronger, energy-intensive result.

“The system was able to come up with these new, rigorous ideas based on the path from the knowledge graph,” said Alireza Ghafarollahi, a postdoc in the Laboratory for Atomistic and Molecular Mechanics (LAMM). “In future work, we’re going to generate thousands, or tens of thousands, of new research ideas… to understand better how these materials are generated and how they could be improved further.”

What Are the Risks of Autonomous AI Systems? 

Autonomous AI systems act autonomously and with less supervision than other systems. However, they are still incapable of human logic and reasoning, leaving them vulnerable to the same dangers that other AI models struggle with. Their reliance on vast datasets exposes them to privacy and cybersecurity risks while raising concerns about system explainability. 

Their automated operations and reinforcement learning process can also amplify certain legal and ethical risks. For example, if left unchecked, an AI agent could:

• Share biased, sensational, and false information by prioritizing social media engagement
• Suggest or implement unethical trading practices in the financial market
• Violate intellectual property laws by using copyrighted material

Companies can mitigate many of these dangers by implementing responsible AI practices, such as ethics committees, audit trails, and other AI governance and accountability measures. 

How Will Agentic AI Impact Businesses? 

Much like generative AI before it, agentic AI is poised to streamline businesses of different shapes, sizes, and industries. According to Gartner, by 2029, AI agents will resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs. 

“Unlike traditional GenAI tools that simply assist users with information, agentic AI will proactively resolve service requests on behalf of customers, marking a new era in customer engagement,” said Daniel O’Sullivan, Senior Director Analyst in the Gartner Customer Service & Support Practice. 

AI decision-making systems may also streamline employees’ day-to-day workflow by automating many complicated but repetitive tasks. AI trading agents, for instance, could improve how financial institutions operate by analyzing market trends and data, then automatically adjusting trading strategies for the least financial risk. McKinsey predicts that the high-quality content these agents produce could reduce review cycle times by 20 to 60%. 

Is Agentic or Traditional AI Better for Your Business?

Agentic and traditional AI models both have a place among organizations. Traditional AI remains highly effective for task-specific automation and data analysis, especially in well-defined, rules-based environments. Narrow AI models also require fewer resources, making them useful for tasks such as fraud detection, predictive maintenance, or automated email sorting.

AI decision-making systems, however, are a strong next step for organizations that need more proactive problem-solving. An AI agent could analyze user tone and preferences to improve their customer service experience. In healthcare, these systems can check patient data to create personalized treatment plans, reducing doctors’ workloads and the risk of burnout. 

Investors have already seen this new technology’s potential, having invested over $2 billion into start-ups focused on enterprise applications in the past two years. As agentic AI continues to improve and develop new applications, companies that understand how and when to apply it to their operations over traditional models will gain an edge in a growing market. 

‍Ready to explore how agentic AI could work for your organization? FullStack Labs can help you build, integrate, and scale intelligent systems tailored to your business needs.