How AI Coding Agents Work: Core Architecture, Agent Harness, and Multi-Agent Systems

Introduction: Understanding the Capability Boundaries of AI Coding Agents

If AI coding tools are treated merely as a “chat window that writes code,” it is easy to fall into two extremes: either overestimating them and assuming they can replace all engineering judgment, or underestimating them as nothing more than advanced code completion. The more meaningful perspective is to look beyond the surface and examine the engineering system known as the Agent Harness.

When evaluating whether an AI coding agent is useful, model capability is only one part. Engineering mechanisms such as tools, context management, permission boundaries, and task scheduling often determine the actual experience.

Many discussions of the underlying architecture of AI coding agents are either too academic and jargon-heavy, or too shallow and stop at “calling an API once.” This article follows a path from the core loop to multi-agent systems and breaks down how these agents work layer by layer, helping developers build a complete understanding.

1. Core Principle: The Model Is the Driver, and the Framework Is the Car

Before discussing any code, one premise must be clear: intelligence comes from the model’s training, not from the framework code you write.

A large model can be understood as a driver, while the agent framework you build is the car. The driver’s skill is determined by training data, and you cannot take control of the wheel for them. However, you can build a good car—one equipped with a steering wheel, dashboard, brakes, and seat belts. This “car” is the Harness. It usually includes four core components:

• Tools: allow the agent to read and write files, execute commands, and retrieve information, upgrading it from “being able to say things” to “being able to do things”;
• Knowledge: domain documents and specifications loaded on demand, avoiding putting all information into the context at once;
• Context management: trimming, summarizing, and compressing conversations as they grow longer, preventing “forgetting”;
• Permission control: limiting what the agent can do and avoiding high-risk behavior such as accidental deletion or modification.

Once this division of responsibilities is understood, each subsequent mechanism can be seen as adding a component to the same vehicle.

2. The Core Agent Loop: One While Loop Drives the Whole System

The most fundamental form of an agent is not a complex neural network, but a simple loop. The pseudocode is as follows:

def agent_loop(task):
    messages = [system_prompt, user_task]
    while not is_done(task):
        response = model.generate(messages)      # 1. The model thinks
        if response.wants_tool:                   # 2. Check whether a tool is needed
            result = execute_tool(response.tool)  # 3. Execute the tool
            messages.append(result)               # 4. Write the result back to the context
        else:
            return response.answer                # 5. Return the answer
    return final_answer

This loop reveals the essence of agent execution: the model is responsible for “thinking and deciding,” while the framework is responsible for “executing and providing feedback.” The two alternate until the task is completed. Although it looks simple, almost all complex capabilities are extended from this skeleton. Understanding this point is the basis for understanding all subsequent mechanisms.

3. Tool Use: Moving the Model from “Saying” to “Doing”

Tool use is the dividing line between an agent moving from “conversation” to “execution.” A model itself can only generate text, while real engineering tasks require reading and writing code, running tests, and querying data.

In implementation, the framework provides the available tools to the model in a structured description, including names, purposes, and parameter schemas. The model selects tools and generates call parameters based on this description, while the framework is responsible for actually executing the tools and returning the results.

A key design here is parameter schema constraints: strict parameter validation can reduce invalid calls caused by model hallucination and also lower the risk of erroneous operations.

A practical suggestion is that tool granularity should be neither too fine nor too coarse. If tools are too fine-grained, the model must repeatedly choose among many small tools, increasing decision cost. If they are too coarse, a single call carries too much logic, making errors difficult to locate. A reasonable division should match real workflows, with each tool corresponding to a clear and verifiable action.

4. Context Management: Engineering Strategies Against “Forgetting”

The model context window is limited, while conversations and file contents in real tasks can expand rapidly. Without management, the system will soon hit length limits or show amnesia-like behavior, such as “reading the end but forgetting the beginning.”

Common context management strategies include:

• Rolling summaries: compress and summarize earlier conversations while retaining key conclusions;
• Retrieval on demand: instead of loading all documents in advance, retrieve relevant fragments according to the current task;
• Segmented processing: split large tasks into small stages, keeping only necessary context in each stage;
• External memory: store long-term information in external storage and retrieve it when needed.

The purpose of these mechanisms is to keep the agent focused on the information most relevant to the current task within a limited window, rather than simply packing in as much content as possible. Good context management directly determines whether long-running tasks can proceed stably.

5. From Single Agent to Multi-Agent: Three Unavoidable Challenges

As task complexity increases, a single agent may struggle to handle planning, execution, and verification all at once. At this point, introducing a multi-agent architecture becomes a natural choice, but three practical problems must be solved:

1. Context explosion: multiple agents each hold large amounts of context, and aggregation can easily exceed the context window. A clear information handoff protocol is needed to pass only conclusions rather than the entire process.
2. Task orchestration: questions such as what should run first, which tasks can run in parallel, and how dependencies should be handled require a unified scheduler.
3. Concurrency conflicts: when multiple agents modify the same file or resource at the same time, locks, version control, or task isolation are needed to avoid overwriting each other.

6. Subagents, Memory, and Task Scheduling

Building on the challenges above, mature agent systems usually introduce several mechanisms:

• Subagents: the main agent delegates subtasks to specialized subagents. Each subagent has its own context and workspace and returns only conclusions after completion, isolating context pollution and keeping the main workflow clean.
• Memory: distinguish short-term memory within the current session from long-term memory persisted across sessions. User preferences, project conventions, and similar information can be accumulated to avoid repeated communication and repeated mistakes.
• Task scheduling: organize work as a task graph or directed acyclic dependency graph, identify branches that can run in parallel, and maximize execution efficiency while ensuring dependency correctness.

7. Multi-Agent Collaboration and Engineering Deployment

The core of multi-agent collaboration is to let different roles handle their respective responsibilities—for example, a planner breaks down tasks, an executor implements code, and a reviewer verifies quality. Agents communicate through structured message protocols to reduce ambiguity and back-and-forth confirmation.

In real engineering deployments, supporting infrastructure is also essential. Object storage can be used to store large files and intermediate artifacts, message queues can decouple asynchronous communication between agents, container platforms can isolate execution environments for security and reproducibility, and logging services and monitoring/alerting systems can track the behavior of each call.

For example, Baidu AI Cloud offers general cloud capabilities such as object storage, message queues, container platforms, logging services, and monitoring and alerting. These can support the stable operation of such agent systems, allowing developers to focus on business logic rather than low-level operations.

Conclusion

Returning to the original question: Are AI coding agents really that powerful?

The answer is that their power comes from the combined effect of model intelligence and engineering Harness. The model determines the upper limit of capability, while engineering mechanisms such as tools, context management, permission control, and task scheduling determine how reliably that capability can be realized in practice.

For developers, understanding this underlying operating logic is more important than chasing a specific tool. Once mastered, it allows you to keep seeing the essence of the problem amid rapidly evolving tools.