AI Agent Architecture: Why Different Agents Share the Same Core Loop

From Confusion to Insight: The Model Is Not the Agent

After using many different AI coding and task assistant tools, a counterintuitive conclusion becomes clear: although these tools may look very different, their underlying architecture is almost the same.

To understand this, we first need to separate two concepts that are often conflated: the model and the agent.

What Is a Model?

A large language model is essentially a function: it takes a sequence of tokens as input and generates the next sequence of tokens. It is strong at understanding language, reasoning, and generating content, but it has two inherent limitations:

• It is stateless. Unless external code feeds the conversation history back in, it cannot remember what was said before.
• It has no hands. It cannot directly read your files, execute a command, access an API, or know whether an action succeeded.

In other words, a model is a highly capable brain trapped in a room with no senses and no limbs.

What Is an Agent?

An agent—also called scaffolding, shell, or harness—is the software layer that connects this brain to the real world. It prepares context, provides tools, executes actions, and returns the results to the model. The model thinks; the agent acts and feeds information back.

This leads to a basic but often overlooked distinction: a large model and an agent built on that model belong to different layers. The same model can be placed inside different agents, and the same agent can be paired with different models. Separating these two layers is the starting point for understanding the entire field.

The Four Core Responsibilities of an Agent

If we strip away all interfaces and branding, what does an agent actually do? Its work can be summarized as four responsibilities, none of which can be missing.

1. Context Preparation

Because the model is stateless, the agent must assemble the context before each call. This includes the task description, system instructions, conversation history, relevant file contents, retrieved materials, and the results of previous tool executions.

The quality of context management directly affects the quality of the model’s output. At its core, this is an engineering problem: putting the right information in front of the model at the right time and in the right form.

2. Tool Provisioning

The agent needs to declare the actions that the model can invoke in a structured format, usually in the form of a tool list. Each tool includes its name, purpose, and parameter structure.

Common tools include reading files, writing files, executing commands, retrieving information, and calling external APIs. Based on this, the model decides which tool to use and what parameters to pass. Clear tool design directly affects whether the model can select tools correctly.

3. Tool Execution

The model only says what should be executed. The actual execution is performed by the agent. It must run the command, send the request, or modify the file in a real environment, such as a local process, an isolated sandbox, or a container.

This is the model’s only channel to reach the real world, and it is often the area where safety matters most. Execution permissions, isolation boundaries, and confirmation of dangerous operations should be controlled at this layer.

4. Observation Feedback

After tool execution, the agent must write the output—command stdout, file contents, API responses, error messages—back into the context so the model can see it.

The model uses this result to determine whether the task has been completed and what to do next. This step closes the loop.

Why Different Agents Share the Same Underlying Architecture

Putting the four responsibilities together gives us the same core loop shared by all agents:

The model thinks → requests a tool → the tool executes → the result is fed back → the model thinks again.

This repeats until the task is completed or the maximum number of steps is reached.

This is why many agents, despite looking different, share the same underlying architecture. Their differences are almost entirely in the implementation details of this loop, not in the loop itself:

• How context is managed: how much history is included, how it is compressed, and how additional information is retrieved.
• Which tools are provided: general execution tools or specialized tools for specific scenarios, such as coding or data analysis.
• Where execution happens: local, sandbox, or cloud, with different isolation and permission strategies.
• Which underlying model is used: different models have different reasoning and generation capabilities, which can significantly affect performance.

In other words, differences in appearance are differences in configuration and implementation details, while the skeleton is the same. Once you understand this, you will no longer be distracted by the surface of a tool. Instead, you can see exactly what trade-offs it makes within this loop.

Seeing the Skeleton Through Pseudocode

The following pseudocode removes all specific tools and branding and keeps only the loop skeleton. You will find that almost any agent has roughly this shape:

def agent_loop(model, tools, task, max_steps=20):
    # 1. Prepare context: build the initial context
    messages = [
        {"role": "system", "content": "You are a task assistant. Call tools as needed."},
        {"role": "user",   "content": task},
    ]
    for _ in range(max_steps):
        # 2. The model decides the next action based on the current context
        response = model.chat(messages, tools=tools)
        # 3. If the model no longer requests tools, treat the task as complete
        if not response.tool_calls:
            return response.content
        # 4. Execute the tool calls requested by the model in the real environment
        messages.append(response)
        for call in response.tool_calls:
            result = execute_tool(call)      # Where the model actually "acts"
            messages.append({
                "role": "tool",
                "content": str(result),       # 5. Feed the result back into the next round
            })
    return "Maximum steps reached; the task did not converge"

The four actions—prepare → chat → execute → observe—are the code-level expression of the four responsibilities. Once you understand this, you have a general standard for judging any agent tool.

The Practical Value of This Mental Model

Separating the model from the agent and seeing the common skeleton is not just a conceptual exercise. It has three practical benefits.

Evaluate Tools by Their Essence

When facing a new tool, you do not need to be led by marketing language. You only need to ask four questions:

• How does it manage context?
• Which tools does it provide?
• Where does it execute?
• Which model does it use?

The combination of these answers roughly outlines the tool’s real capabilities.

Match Tools to Your Needs

If your core requirement is security isolation, focus on the execution environment and permission strategy. If you care about efficiency in a specific domain, focus on specialized tools and context design.

The model can be replaced, but the trade-offs in the skeleton are relatively stable.

Build and Customize With Confidence

If you want to build your own agent, you do not need to start from scratch. Build a loop using the four stages: context preparation, tool provisioning, tool execution, and observation feedback. Then gradually improve context retrieval, the tool set, and execution safety.

If elastic and isolated execution environments are involved, you can reuse general cloud infrastructure such as container platforms, object storage, message queues, logging, and monitoring/alerting capabilities. This lets you focus on the loop logic itself.

Conclusion

The most important lesson after using these tools for a long time is: do not be distracted by a model’s reputation or a tool’s shell.

The model is the core, and it determines how smart the system can be. The agent is the shell, and it determines how deeply the system can reach into the real world.

Agent tools with very different forms share the same core loop of thinking → executing → feeding back. Their differences lie only in configuration and implementation details.

Once you grasp this skeleton, you will be more confident when evaluating, selecting, and building agents—because what you see is no longer a collection of isolated products, but different projections of the same architecture under different trade-offs.