In recent years, AI development has shifted from monolithic large models — designed to process data based on their training — to more modular AI systems capable of responding with real-time information. Updating monolithic models is both costly and resource-intensive, making adaptability a key priority in the evolution of AI.
Today, AI models exist across a broad spectrum. Some are general-purpose, while others have access to user profiles, enabling them to provide more personalized responses. More advanced models can even break down complex problems and approach them differently compared to earlier iterations. Each of these systems is designed with trade-offs, balancing flexibility, efficiency, and accuracy based on the needs of the project.
The shift in AI technology can be broadly categorized into three stages:
- Traditional Large Language Models (LLMs): These models process information based on extensive training datasets but have limitations in adaptability.
- Compound AI Models: These leverage system design principles to better structure responses and interact with external tools.
- ReACT Agentic Models: Inspired by compound AI, these models take a different approach by autonomously planning and iterating on their decisions before execution.
Each of these advancements offers distinct advantages and challenges.
Compound AI models surpass traditional monolithic systems by allowing custom logic to perform specific workflows. Unlike static models, they can integrate external tools, such as fetching real-time vacation data for an employee or tracking live stock market movements to generate weighted responses. However, these models have inherent limitations — since their logic is hardcoded into system flows, they excel in well-defined tasks but struggle when faced with problems beyond their predefined scope.
In contrast to compound AI, the ReACT Agentic approach leverages LLMs to make decisions dynamically. Instead of following a pre-set workflow, these models break down complex problems, develop multiple plans, and refine them before execution. While this process deliberately slows down the response time compared to compound AI, it significantly enhances accuracy. On the speed-versus-accuracy scale, ReACT Agentic AI prioritizes precision, making it more suitable for tasks requiring detailed, well-structured outputs.
To improve accuracy, agentic AI models integrate multiple external systems, calling upon relevant resources in real time. Many companies now see this approach as the next step toward business-ready AI, offering a more adaptive and decision-driven intelligence framework.
As AI continues to evolve, the shift from static monolithic models to dynamic, agentic AI marks a significant leap forward. With enhanced problem-solving capabilities and greater adaptability, AI agents are poised to redefine how businesses and individuals interact with technology.