The evolution of AI mirrors the trajectory of software development paradigms and distributed computing architectures, each stage building on the foundations of the previous one to achieve greater scalability, specialization, and autonomy. Starting with OpenAI’s release of advanced language models (LLMs) in October 2022, this journey reflects a shift from monolithic intelligence to highly distributed, orchestrated, and autonomous systems.
The release of OpenAI’s GPT-3 and GPT-4 introduced foundational LLMs capable of understanding and generating human-like text. These models were centralized, monolithic systems—powerful, but essentially self-contained. Their utility depended on clear, step-by-step prompts, much like procedural programming relies on linear, explicit instructions. This stage provided the groundwork but lacked context-specific adaptability, requiring external human effort to direct outcomes.
Parallel in Software Development: Procedural programming focused on linear logic and reusable routines but lacked the modularity and scalability that object-oriented programming introduced.
The next evolution was the emergence of assistant AIs, such as GitHub Copilot and Microsoft’s copilots across Office products. These systems became context-aware, embedding into specific workflows like coding, document creation, and analytics. By leveraging specialized "objects" (tasks or domains), these copilots streamlined processes by anticipating user needs, similar to how object-oriented programming organizes data and behavior into reusable classes. While still dependent on user direction, they began to act as extensions of human effort.
Parallel in Software Development: Object-oriented programming introduced encapsulation, allowing systems to model real-world entities as objects that interact. Copilots emulate this principle by focusing on domain-specific tasks.
As LLMs evolved, they started to develop agentic capabilities, moving beyond passive assistance to active execution of tasks. These agents can complete specific goals independently (e.g., writing code, analyzing datasets, or managing repetitive workflows). They act like APIs—exposing functionality that allows modular, programmatic interaction. A user or system defines the objective, and the agent handles the implementation, often leveraging external tools.
Parallel in Software Development: APIs decoupled system functionality from monolithic applications, enabling modularity and interaction between systems.
The concept of agentic swarms introduces multiple specialized AI agents working in parallel, orchestrated by a master agent. This is analogous to microservices architecture, where individual services perform specific tasks and communicate through orchestration layers like Kubernetes. For example, one agent may retrieve data, another processes it, and a third visualizes it, all working in concert. These swarms are more resilient and scalable, addressing tasks too complex for a single agent.
Parallel in Software Development: Microservices replaced monolithic applications by decentralizing services into discrete, independent units, allowing scalable and fault-tolerant systems.
The ultimate evolution is small, autonomous code agents that operate independently of centralized control, akin to edge computing. These agents are deployed in specific environments, executing tasks with minimal oversight and dynamically adapting to changing conditions. Such agents may work in real-time, decentralized networks—e.g., optimizing supply chains, managing IoT systems, or executing black-box optimizations where intermediate processes are opaque but outcomes are validated.
Parallel in Computing Architectures: Edge computing pushes intelligence closer to the data source, reducing latency and dependency on centralized systems. Autonomous agents mirror this principle by executing tasks locally and autonomously.
AI's evolution parallels the movement in computing from monolithic mainframes to distributed, client-server, and eventually edge computing. Initially centralized, systems evolved to leverage the ubiquity of the cloud and now increasingly push decision-making to edge devices like phones and IoT hardware. Similarly, AI is moving from centralized, general-purpose models to distributed, specialized agents capable of functioning independently or collaboratively.
For data architects and AI engineers, these shifts demand an adaptive mindset:
The journey from monolithic LLMs to autonomous agentic systems represents a move toward AI systems that mimic not just human intelligence but also the adaptability and modularity of modern software ecosystems.