The Difference Between LLMs and Agents

2026-03-25 · Source: MLOps.community · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The core distinction between a Large Language Model (LLM) and an LLM-enabled agent lies in the control flow mechanism. In agentic systems, the LLM itself dictates subsequent actions, such as whether to continue processing or invoke a specific tool, based on its responses. This contrasts with non-agentic LLM applications, like document processing, where the control flow is pre-defined, applying a fixed sequence of transformations or summaries. Agentic AI, while still leveraging LLMs for value generation, introduces a dynamic decision-making capability where the model determines the next step in a workflow, with systems like Temporal capturing and durably storing these decisions and their outcomes.

Key takeaway

For AI Engineers designing automated systems, understanding the control flow difference between LLMs and agents is crucial. If your application requires dynamic decision-making and tool orchestration, prioritize an agentic architecture where the LLM drives the workflow. Conversely, for predictable, sequential tasks like document transformation, a non-agentic LLM application will suffice, simplifying design and implementation.

Key insights

LLM agents dynamically control workflow decisions, unlike non-agentic LLM applications with pre-defined control flows.

Principles

• Agentic AI empowers LLMs to dictate next actions.
• Non-agentic AI follows pre-defined operational sequences.

Method

Agentic systems use LLM responses to decide workflow progression, tool calls, and subsequent steps, with state captured durably.

In practice

• Implement LLM agents for dynamic task execution.
• Use non-agentic LLMs for structured data processing.

Topics

• Agentic AI
• Non-Agentic AI
• LLM Control Flow
• AI Workflow Management

Best for: AI Engineer, Machine Learning Engineer, AI Architect

Related on AIssential

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• When to use Small LM for AI Agents: New Insights — Small, open-weight LLMs can handle many AI agent tasks, significantly reducing operational costs.
• You Probably Don’t Need an Agent Framework — Explicit LLM workflows offer more reliability and control than autonomous agents for many applications.
• LLM Vs AI: A Practical Guide to Differences, Use Cases, and Tools — Generative AI and LLMs are transforming enterprise operations through diverse applications, requiring careful selection and deployment.
• Beyond the Chatbot: How AI Learned to Think, Plan, and Act for You — Agentic AI combines LLMs with planning and external tools to execute complex, multi-step tasks autonomously.
• Our New Agentic AI Engineering Course! — Building production-ready AI agents requires extensive engineering beyond basic LLM calls.

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Editorial summary, takeaway, and curation by AIssential. Original article published by MLOps.community.