TL;DR: An LLM is a stateless, request-response engine that processes inputs to generate outputs. An AI agent wraps this model in an execution loop and equips it with tools, allowing the model to make sequential decisions, observe outcomes, and act autonomously to achieve a goal.
The line between a Language Model (LLM) and an AI agent is frequently blurred by marketing hype and interchangeable industry jargon. As engineers, we need to understand the structural boundaries of these systems to build, debug, and scale them effectively.
Here is the technical distinction between a raw language model and an autonomous agentic architecture.
What is the difference between an LLM and an AI Agent?
An LLM is a stateless, request-response engine that maps inputs to outputs. An AI agent wraps that LLM in a continuous execution loop, giving it access to tools and the ability to make sequential decisions until a goal is met.
Think of a standard Large Language Model (LLM) as an advanced pure function. You pass text, images, or video in, and you get text, images, or video out. It does not remember the last API call unless you pass the chat history back to it, and it cannot interact with external systems on its own.
An agent takes that request-response model and puts it inside an execution loop. The loop structure runs sequentially:
- Observe: Inspect the current state of the environment.
- Reason: Send the state to the LLM and ask, "What action or tool should we use next?"
- Act: Execute the tool chosen by the LLM.
- Repeat: Take the output of that action, update the environment state, and run the loop again.
How does an AI Agent use tools in a loop?
An agent uses tools by evaluating its current state against a target goal, deciding which tool fits best, and executing that tool to change the state. The LLM does not run the tool itself; instead, it outputs structured data specifying which tool to invoke and with what parameters.
To visualize this, imagine a physical robot equipped with a camera, wheels, and an onboard computer running a continuous loop. The system takes a camera snapshot, sends it to the LLM, and asks, "Do you want to go forward, left, or right?" The LLM analyzes the snapshot and responds, "forward." The system then executes that physical motor command, moves forward one step, takes a new screenshot, and repeats the process.
The intelligence of the system is the request-response model. The agent is the surrounding loop that captures state, calls the model, and translates the model's text decision into a real-world tool execution.
What tools do developers use to run AI Agents?
Most developers interact with agents daily through terminal and IDE tools like Claude Code, Cursor, or Devin. These systems read your local files, determine necessary edits, run terminal commands, and inspect the outcomes autonomously.
| Agent Tool | Primary Use Case | How It Uses the Loop |
|---|---|---|
| Cursor / Claude Code | Code editing and debugging | Reads your codebase, plans edits, writes files, and runs tests to verify the fix. |
| Codex / Copilot Workspace | Task-scoped software engineering | Takes an issue description, drafts a plan, modifies code, and submits pull requests. |
| Local custom scripts | Custom developer workflows | Orchestrates local shell commands, APIs, and databases based on your CLI inputs. |
At their most basic level, these developer tools simply run commands on your machine. They inspect the state of your workspace, pass that context to an LLM, ask what files to modify, execute those changes, and review the linter or compiler errors to decide if they need to make further edits.
How do you build a custom AI Agent?
Building a basic agent does not require complex orchestration frameworks; it only requires writing a standard control loop around an LLM API. You can write this from scratch in Python or Node.js by defining tools as helper functions and allowing your prompt to select them.
While production-grade frameworks exist, writing your first agent from scratch is the best way to demystify the technology. You define your tools as basic functions, describe those functions in your system prompt, and write a simple parser that invokes the correct function when the LLM requests it.
It is an incredibly straightforward architecture to experiment with, and writing your own loop is highly educational.
Cheers!
FAQ
Do AI agents require specialized machine learning models?
No. You can build an agent using any standard LLM API (like OpenAI, Anthropic, or local models via Ollama) as long as you wrap the API in an execution loop and write the code to handle tool execution.
What is the difference between tool calling and an AI agent?
Tool calling is a single-step feature where an LLM suggests a tool and arguments to run. An AI agent is the broader system architecture that actually executes that tool, captures the output, and feeds it back into the model in a continuous loop to achieve a long-term goal.
Are AI agents safe to run locally on my computer?
They can be risky if given unrestricted terminal access. Because agents can write and execute arbitrary code, it is best practice to run them in sandboxed environments, Docker containers, or with explicit user-approval prompts before executing destructive commands.
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