How Coding Agents Work: A Deep Dive into Their Architecture and Runtime Logic

1. From Chatting Models to Working Agents

Over the past couple of years, as large models have continued to advance, a class of tools known as coding agents has entered the developer’s field of view. Many people’s first reaction is to equate them with “more powerful code assistants,” but from an engineering perspective the two are fundamentally different.

An ordinary code assistant works more like a question-and-answer exchange: you provide a prompt, and the model returns a snippet of code or an explanation. A coding agent, by contrast, can autonomously plan tasks, call tools, read and modify files, execute commands, and adjust its strategy based on results—all the way until a complete goal is achieved. It no longer passively responds, but actively drives the task forward.

To understand the runtime logic behind this, we first need one core idea:

A coding agent’s capability depends not only on the underlying large model itself, but more on the layer of system architecture wrapped around the model.

This layer is usually called the agentic harness. It treats the model as a “decision engine” and, through a carefully designed loop, context, and tools, converts the model’s language ability into practical software engineering behavior. Understanding it is the key to grasping how coding agents operate.

2. The Core: The Agentic Harness

You can think of a coding agent as having three layers:

• The model layer at the bottom: responsible for reasoning, understanding code, and generating solutions—the “brain.”
• The orchestration layer (agentic harness): responsible for scheduling the model, managing context, parsing output, and driving tool execution—the “nervous system.”
• The tool layer: capabilities such as file reading and writing, command execution, and code retrieval—the “hands and feet.”

Most people focus only on how strong the “brain” is, yet overlook the fact that the design of the “nervous system” and the “hands and feet” is what really determines the actual experience. Given the same underlying model, an agent with a well-designed orchestration layer can reliably complete large refactors spanning multiple files, while a crudely built implementation often falls into disarray after just a few steps.

3. The Main Loop: Perceive—Decide—Act—Observe

The most fundamental runtime logic of a coding agent is a continuously repeating loop. The industry usually abstracts it into four stages:

1. Perceive: assemble the user’s goal, the current code context, and the results of past operations into the model’s input.
2. Think/Plan: the model analyzes the current situation and plans the next action, which might be an analysis, a tool call, or a self-correction.
3. Act: parse the model’s output and trigger the corresponding tool (such as reading a file, modifying code, or running tests).
4. Observe: feed the tool execution results (file contents, command output, error messages) back to the model and enter the next round.

This loop keeps running until the model judges the task complete, needs user confirmation, or hits a stopping condition. Below is a minimal, illustrative implementation to help you understand the loop structure:

def agent_loop(task: str, max_steps: int = 30):
    # Maintain conversation history as the agent's "short-term memory"
    messages = [
        {"role": "system", "content": "You are a senior software engineer who completes tasks through tools."},
        {"role": "user", "content": task},
    ]
    for step in range(max_steps):
        # 1. Decide: let the model output the next action (text or tool call)
        response = model.generate(messages, tools=TOOL_SCHEMA)
        # 2. Check whether to stop
        if response.finish_reason == "stop":
            return response.text
        # 3. Act: execute the tool calls requested by the model
        for call in response.tool_calls:
            result = dispatch_tool(call.name, call.arguments)
            # 4. Observe: write the execution result back into the context
            messages.append({
                "role": "tool",
                "name": call.name,
                "content": str(result),
            })
        # Also add the model's reasoning/request to the history
        messages.append({"role": "assistant", "content": response.text})
    raise RuntimeError("Reached the maximum number of steps; task not completed")

This code looks simple, yet it distills the skeleton of agent operation: the model does the thinking, the orchestration layer handles execution and feedback, and the loop connects the two into a closed loop. Real systems add a great deal of engineering detail on top of this, but the main loop stays the same.

4. Context Engineering: Letting the Model “Understand” the Entire Codebase

Every model has a natural limit—the context window. Yet a real code repository easily contains hundreds of thousands of lines of code, which cannot all be crammed into the context at once. Context engineering therefore becomes one of the keys to whether a coding agent succeeds.

It mainly solves three problems:

• Retrieval and filtering of relevant code: through keyword matching, semantic retrieval, or structured indexing (such as building a code symbol graph), only the files, functions, and type definitions most relevant to the current task are injected into the context, avoiding “information overload.”
• Context compression and summarization: apply layered summarization to overly long conversation histories and file contents, keeping key decisions and conclusions while discarding redundant details.
• Dynamic assembly: before each round of the loop, decide anew which context is needed as the task progresses, rather than filling the context all at once.

A practical principle: context is not better when there is more of it, but when it is more precise. Irrelevant information not only consumes budget but also distracts the model’s attention, lowering output quality.

5. Tool Design: The “Hands” for Interacting with the Code World

Tools are the only way an agent affects the external world, and the quality of tool design directly determines the boundaries of the agent’s capability. Common families of tools include:

• File operations: reading, creating, editing, and locating modifications by line number.
• Command execution: running builds, tests, static checks, and scripts.
• Code retrieval: precise search by symbol, reference relationships, or filename.
• Structured queries: parsing syntax trees and obtaining function signatures and dependency relationships.

Some important lessons when designing tools:

1. Moderate granularity: a tool that is too coarse (such as “finish the whole project for me”) makes it hard for the model to control, while one that is too fine significantly increases the number of loop steps. A good tool should correspond to a clear, verifiable atomic action.
2. Clear feedback: tool results should be structured and readable. When a command errors out, keep the full stack trace to help the model locate the problem.
3. Safety boundaries: set confirmation mechanisms or sandbox isolation for dangerous operations (deleting files, executing arbitrary commands) to prevent irreversible loss from mistakes.

The relationship between tools and the model is “convention over coercion”: clear tool descriptions (name, parameters, return value, usage scenarios) are themselves a prompt to the model and directly affect how correctly it calls the tools.

6. Prompt Caching and Runtime Stability

In long-running, multi-turn agent sessions, one easily overlooked yet hugely impactful factor is prompt caching.

Each round of the agent loop resends a large amount of context, in which the system prompt, tool definitions, and code snippets are often highly repetitive. If the model has to recompute these fixed prefixes from scratch on every request, it not only wastes compute and adds latency but also drives up cost. Prompt caching significantly reduces this repeated overhead by reusing the computed results of a stable prefix.

To maximize the cache hit rate, engineering practice needs to keep the prefix stable:

• Put the system prompt and tool definitions at the very front, and avoid unnecessary changes.
• Place dynamic content (user input, tool results) at the end of the context so the prefix is not disturbed.
• Avoid arbitrarily reordering or rewording early messages between rounds.

Cache stability and context engineering are two sides of the same coin: both improve performance and lower cost, and both keep the agent behaving consistently over long sessions.

7. Memory and Long-Session Continuity

Real development tasks often span dozens of tool calls and can last for hours. This requires the agent to have memory and long-session continuity.

Memory is usually divided into two types:

• Short-term memory: the conversation history and operation trajectory within the current session, used to maintain the continuity of “what I am doing right now.”
• Long-term memory: knowledge persisted across sessions, such as project structure notes, coding conventions, and records of key decisions, usually stored via files or vector databases and retrieved and fed back on demand.

Common strategies for maintaining long-session continuity include:

• History summarization: when the conversation approaches the context limit, compress early content into a structured summary.
• Task state tracking: explicitly maintain a “to-do list” or “progress record” so the model always knows what has been done and what remains.
• Periodic self-check: have the agent review its goal at key points to avoid “drifting further off track.”

8. Engineering Practice Recommendations

If you want to build your own coding agent, you can start from the following principles:

1. Get the minimal loop working first, then enhance it step by step: a simple agent that reliably closes the loop is better than a feature-rich system that frequently crashes.
2. Treat prompts as engineering assets: the system prompt and tool descriptions need version control and continuous iteration.
3. Prioritize observability: fully recording each step’s model input and output, tool calls, and timing is the foundation for debugging and optimization.
4. Security and permission tiering: set least privilege for the resources the model can reach, and introduce human confirmation for sensitive operations.
5. Keep costs under control: combine prompt caching, context streamlining, and reasonable stopping conditions to avoid falling into meaningless loop consumption.

9. Conclusion

The underlying runtime logic of a coding agent can be summed up in one sentence: it is a closed-loop system with the model as its decision-making core, the agentic harness as its skeleton, and tools as its hands and feet. The model provides intelligence, but what really determines the experience and reliability are the “peripheral architecture” elements: context engineering, tool design, cache stability, memory mechanisms, and loop control.

Once you understand this logic, you will find that building an excellent coding agent is less about “tuning the model” and more about doing rigorous systems engineering. Mastering these underlying principles, whether you are selecting, optimizing, or building your own, will help you make clearer judgments and keep evolving toward genuinely reliable and efficient intelligent programming.