Demystifying Agentic AI: A Simple Guide to the Data Flow

Agentic AI is the tech world’s latest buzzword, but what does it actually mean? How does an AI “agent” work under the hood to accomplish a task?

This post breaks down the core concepts of Agentic AI by walking through a practical, real-world example: automatically fixing bugs in code. By visualizing the data flow, the magic of these systems becomes a clear, understandable process.

The explanation is based on a fantastic video by Don Woodlock (linked at the bottom of this post), which provides a perfect blueprint for understanding this technology.

The Big Picture: An AI That Uses Tools

magine asking a clever assistant to fix a bug in a piece of software, but instead of just giving you an answer, the assistant can look at the files, run the code, and make edits itself. That’s the essence of an agentic system.

The fundamental difference from a standard Large Language Model (LLM) interaction is the loop. A standard LLM gives a one-off response. An agentic system, however, is built on a cycle:

Think -> Act -> Observe -> Repeat.

The Architecture: A Step-by-Step Breakdown

The entire system revolves around a central “Agentic AI Framework” that orchestrates the communication between your application, the LLM, and the tools the LLM can use.

1. The Starting Point: Your Application

It all begins with an application that has a problem to solve. In this case, it’s a bug-tracking app with a list of issues. The goal is to automatically fix one of those bugs.

• The Trigger: The app sends a request to the Agentic AI Framework, asking it to “fix this specific bug.”

2. The Brain: The Initial Prompt

The framework doesn’t just send a vague request. It crafts a detailed initial prompt for the LLM. This prompt is the key to the whole process and contains two critical parts:

• The Instruction: A clear goal, e.g., “Your task is to fix the bug described here: [Bug Description].”
• The Tool List: A description of the tools the LLM is allowed to use. Think of this as giving the LLM a set of hands to interact with the world. For the bug-fixing use case, the tools might include:
  • list_files(directory): To see what code files exist.
  • read_file(filename): To examine the contents of a specific file.
  • edit_file(filename, search_text, replace_text): To modify the code.
  • run_bash_command(command): To execute the code and see if it works.
  • create_file(filename, content): To create a new file, like a test case.
  • done(fix_summary): A special tool to signal that the task is complete and return the final result.

This initial prompt is sent from the framework to the LLM. The LLM now understands its mission and the tools at its disposal.

3. The Core Loop: The Heart of Agentic AI

This is where the “agentic” magic happens. The process is no longer a single request-response. It becomes an autonomous, multi-step loop.

1. The LLM Decides: The LLM analyzes the current state (the bug report, any files it has seen, etc.) and decides on the next logical step. It doesn’t execute the step itself; instead, it responds to the framework with a request to call a specific tool. For example, it might respond with: CALL TOOL: list_files(directory: "/src").
2. The Framework Executes: The Agentic AI Framework receives the LLM’s request. Its job is to safely execute the tool. It calls the list_files function in your actual environment, gets the list of files, and captures the result (e.g., ["main.py", "utils.py", "bug.py"]).
3. The Framework Reports Back: The framework takes the result from the tool and sends it back to the LLM as a new message. “Here is the list of files you requested.”
4. Repeat: The LLM now has new information. It can process the list of files and decide its next action. “Now that I see the files, I want to read_file(filename: "bug.py").” The loop continues:
   • LLM: “Based on the code in bug.py, I think I need to edit_file.”
   • Framework: Executes the edit, reports success or failure.
   • LLM: “Now I should run_bash_command(command: "python bug.py") to test my fix.”
   • Framework: Runs the test, captures the output, and sends it back.

This cycle of LLM decision -> framework action -> observation can happen dozens or even hundreds of times.

4. The Finish Line: Completing the Task

The loop continues until the LLM determines its goal has been met. In this case, after listing files, reading code, making edits, and running tests, it will finally conclude that the bug is fixed. It then triggers the final, special tool.

• The Final Call: The LLM responds: CALL TOOL: done(fix: "Replaced the incorrect variable name in bug.py").
• The Result: The framework calls the done tool, which packages up the final fix and sends it back to the original application. The process is complete.

Why This Approach Matters

The power of this architecture is its simplicity and flexibility. As a developer, you are no longer responsible for pre-programming every single step of a complex workflow. You don’t have to write the logic for “if this file exists, then do that, else do something else.”

Instead, your job is to:

1. Define the goal clearly in the initial prompt.
2. Provide the right tools for the job.

The LLM becomes the reasoning engine, dynamically planning its own steps based on the situation it discovers. As LLMs get better at planning and reasoning, these agentic systems will become exponentially more powerful and capable of handling increasingly complex, open-ended problems.

Watch the Video

The video below contains the original explanation this post was based on.