# Why Large Models Can Be General-Purpose, While Agents Must Be Specialized
In recent years, the rise of Agentic AI and Large Language Models (LLMs) has revolutionized how we approach productivity and automation. Agentic AI, in particular, has captivated many by promising exponential productivity gains. It allows us to focus solely on the *what* — the final deliverable — without getting bogged down in the *how* — the intricate implementation details. This paradigm shift enables a “set and forget” mentality, where multiple tasks can run in parallel, achieving true scalability. However, despite these advantages, there is a fundamental reason why large models can remain general-purpose, while agents tend to be specialized.
## The Allure of Agentic AI: Focus on Deliverables, Not Details
Agentic AI’s appeal lies in its ability to delegate execution details entirely to the AI itself. By defining *what* we want, rather than *how* to do it, we free ourselves from micromanaging every step. This abstraction is powerful: it lets us launch multiple workflows simultaneously, trusting the AI to handle the complexities. The productivity boost is undeniable — no longer do we need to spend hours coding or orchestrating processes; instead, we can concentrate on high-level goals.
This approach aligns perfectly with the concept of scalability. When the AI autonomously manages execution, we can multiply outputs without a linear increase in effort. The promise is clear: more done, faster, with less human intervention.
## The Hidden Challenge: Iteration and Feedback Loops in Agentic AI
Yet, this ideal scenario often clashes with reality. In many cases, after the AI delivers a result, significant time is still required to review, discuss, and refine the output. This iterative process erodes the core advantage of Agentic AI — the ability to “set and forget.” Why does this happen?
The root cause lies in the self-iteration mechanism of Agentic AI. While agents can execute tasks and produce outputs, they lack an intrinsic, objective feedback loop to evaluate the quality of their deliverables. Without a clear success criterion or external feedback, the agent cannot effectively self-correct or improve its results. It may appear to be running iterative cycles, but these loops are blind to whether the product is actually good or not.
This absence of a robust feedback mechanism means the critical “iteration feedback” stage breaks down. The agent cannot sense flaws or deficiencies in its output, nor can it autonomously adjust to meet quality standards. Consequently, the iterative refinement that is essential for high-quality results becomes a bottleneck requiring human intervention.
## Why Large Models Are General-Purpose, But Agents Are Specialized
Large models like LLMs are trained on vast, diverse datasets and designed to generalize across many domains. Their strength lies in their broad knowledge and flexible reasoning capabilities. They can generate text, answer questions, and perform a wide range of tasks without being tailored to a specific function.
In contrast, Agentic AI systems are often built to solve particular problems or workflows. Their specialization stems from the need to incorporate domain-specific knowledge, success criteria, and feedback mechanisms to effectively iterate and improve. Without these, agents cannot reliably deliver high-quality results autonomously.
Therefore, while large models serve as versatile, general-purpose engines, agents must be specialized to harness their full potential. The specialization enables them to embed the necessary feedback loops and evaluation metrics that large models alone do not possess.
## Conclusion
Agentic AI offers a compelling vision of productivity by abstracting away execution details and focusing on deliverables. However, the lack of intrinsic, objective feedback mechanisms limits agents’ ability to self-iterate and refine outputs autonomously. This fundamental challenge explains why large models can remain general-purpose, while agents must be specialized to deliver consistent, high-quality results.
Understanding this distinction is crucial for effectively leveraging AI technologies. By recognizing the strengths and limitations of both large models and agentic systems, we can better design workflows that maximize productivity and quality.
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*Keywords: Agentic AI, LLM*
Created by https://agentics.world