The Rise of Large Language Models and the Shift to Agentic Systems

The Core Idea

Deep learning relies on representing data across layered feature spaces.

This approach allows complex patterns to be learned from vast amounts of information, forming the basis for sophisticated AI systems.

Business Advantage – Transitioning from ‘Request/Response’ to Automated Processes

The rapid evolution of large language models (LLMs) is moving beyond simple text-based chatbots towards multi-component agentic systems that interact with tools, APIs, and local data.

This shift focuses on completing full workflows rather than simply answering queries, demanding models to plan actions, verify results, and adjust their strategies.

Overview of Trends: Composition of Roles (Planner, Executor, Critic, Reflector)

Key technical trends include multi-modality (text+images+audio+video), increased inference efficiency, fine-tuning for specific domains, and robust protocols for tool utilization (function calling, tool use).

Agentic capabilities are strengthened through planners, executors, reflectors, and critics – roles that distribute tasks among submodules.

Frequently asked questions