LLMs vs Agents vs Agentic AI

The world of AI is rapidly evolving, and terms like LLM (Large Language Model), agent, and Agentic AI are becoming central to modern applications. This guide will clarify what each means, how they differ, how they are implemented, and why they matter. Visual diagrams are included for clarity.

---

What is an LLM (Large Language Model)?

An LLM is a type of artificial intelligence model trained on vast amounts of text data to understand and generate human-like language. Examples include OpenAI's GPT-4, Google's Gemini, and Meta's Llama.

Key Features:

• Can generate, summarize, translate, and answer questions in natural language

• Trained on billions of words from books, articles, and the web

• Does not have memory or goals—responds to each prompt independently

Visual: LLM as a Black Box

graph TD
    Input[Prompt/Text Input] --> LLM[LLM]
    LLM --> Output[Text Output]

---

What is an Agent?

An agent is a system that uses an LLM (or other AI models) as a core component, but adds the ability to:

• Make decisions

• Use tools (APIs, calculators, web search, etc.)

• Maintain memory or context

• Take actions in a sequence to achieve a goal

Agents are the "brains" that orchestrate LLMs, tools, and logic to solve more complex tasks.

Visual: Agent Architecture

flowchart TD
    User[User Input] --> Agent
    Agent -->|Prompt| LLM
    Agent -->|Tool Use| Tool[External Tool/API]
    Agent -->|Memory| Memory[Short/Long-Term Memory]
    LLM --> Agent
    Tool --> Agent
    Memory --> Agent
    Agent --> Output[Final Output]

---

What is Agentic AI?

Agentic AI refers to systems where agents are not just passive responders, but actively pursue goals, plan, reason, and adapt. Agentic AI can:

• Autonomously break down tasks into sub-tasks

• Choose which tools or models to use

• Learn from feedback and update strategies

• Collaborate with other agents or humans

Agentic AI is a step toward more autonomous, intelligent systems that can operate in dynamic environments.

Visual: Agentic AI Workflow

flowchart TD
    Goal[User/Business Goal] --> Planner[Agentic AI Planner]
    Planner --> Subtasks[Task Decomposition]
    Subtasks --> Agents[Multiple Agents]
    Agents --> Tools[Tools/LLMs/APIs]
    Tools --> Agents
    Agents --> Aggregator[Aggregator/Coordinator]
    Aggregator --> Result[Final Output]

---

Key Differences

---

Implementation Overview

LLMs

• Built and trained by AI labs (OpenAI, Google, Meta, etc.)

• Accessed via APIs (e.g., OpenAI API)

• Used as a service in applications

Agents

• Implemented using frameworks like LangChain, Semantic Kernel, or custom code

• Combine LLMs with tool integrations, memory modules, and decision logic

• Can be stateless or stateful

Agentic AI

• Built on top of agent frameworks

• Includes planning modules, feedback loops, and multi-agent orchestration

• May use vector databases, knowledge graphs, and advanced memory

Visual: Technology Stack

flowchart TD
    LLM[LLM API/Model] --> AgentFramework[Agent Framework]
    AgentFramework --> AgenticAI[Agentic AI System]
    AgenticAI --> App[End-User Application]

---

Use Cases

• LLM: Text generation, summarization, Q&A

• Agent: Chatbots with tool use, workflow automation

• Agentic AI: Research assistants, autonomous coding, multi-step reasoning

---

Conclusion

LLMs are the foundation, agents are the orchestrators, and Agentic AI is the next frontier—enabling systems that can plan, reason, and act autonomously. Understanding these layers is key to building the next generation of intelligent applications.

---

For more information, explore the LangChain and Agentic AI documentation and research papers.