Desktop AI Agent Technical Routes: Computer Use vs. Multi-Agent Local Code Execution

Introduction: Agents Are Moving from “Saying” to “Doing”

Over the past two years, the capability boundary of AI assistants has been continuously redefined: from answering questions and generating text to calling tools and executing code. When an agent starts to run directly on a developer’s or user’s local computer, and can directly operate files, browsers, and various applications, its form changes fundamentally. It is no longer merely “an object of conversation,” but “an executor that can take action.”

Desktop agents differ most significantly from cloud-based conversational products in that they have actual control over your local environment. It is precisely the way this control is implemented that has given rise to two distinct technical routes. Understanding the principles, boundaries, and trade-offs of these two routes is more valuable than debating which product is stronger.

1. The Essence of Desktop Agents: Taking Over the Final Step of Human-Machine Interaction

Traditional software automation depends on two things: stable API interfaces, or explicit scripting commands. But in reality, many enterprise internal systems, legacy desktop software, and cross-system approval workflows simply do not expose interfaces. To achieve automation in such environments, the only path is to simulate human operation: looking at the screen, moving the mouse, clicking, and typing.

The core value of a desktop agent is to fill the “last mile” of automation. The essential problem it solves is: enabling AI to complete end-to-end tasks like a human when standard interfaces are unavailable. Around this goal, the industry has gradually split into two implementation approaches: one begins with “perception,” allowing AI to understand the screen; the other begins with “execution,” allowing AI to write code that runs locally.

2. Route One: Visual Control (Computer Use)

What It Is

This route is usually referred to as Computer Use. Its core mechanism is this: instead of calling functions through interfaces provided by software, it operates software like a real person does — “looking at the screen,” identifying buttons, input fields, menus, and text on the page, and then generating simulated mouse and keyboard operations to drive the software.

In simple terms, the agent’s working loop is roughly as follows:

loop:
    screenshot = capture the current screen
    elements   = vision model identifies UI elements (buttons / inputs / text)
    plan       = large model decides the next action based on the task goal
    action     = generate and execute an operation (move / click / type / scroll)
    observe the result, decide whether the task is complete, otherwise continue the next loop

Fundamental Difference from “Record and Replay” Tools

Many people confuse this approach with earlier automation record-and-replay tools, but the two are fundamentally different. Record-and-replay tools “record once and replay the same way”; they fail when the interface changes, and are essentially rigid scripts. Visual control, however, is based on “real-time understanding and dynamic decision-making.” At every step, it re-observes the screen state before deciding the next action. The former depends on the interface remaining exactly unchanged, while the latter has a certain degree of fault tolerance and adaptability.

Key Technical Components

• Visual understanding: Multimodal models are required to convert screenshot pixels into structured UI element information.
• Task planning: High-level goals, such as “submit this week’s expense reports,” need to be decomposed into a series of executable atomic actions.
• Action execution and error correction: After execution, the result must be observed. If a click does not take effect or the page navigation behaves abnormally, the agent should be able to recognize this and retry or roll back.

Capability Boundaries and Limitations

Its greatest advantage is “generality.” In theory, it can operate any software with a graphical interface, even systems that are decades old. But it also has clear weaknesses. First, it is slow, because every step requires taking a screenshot and performing inference. Second, it is fragile; subtle changes in interface resolution, themes, or pop-ups may cause misjudgment. Third, the risk is high, because it clicks and types directly in your real environment, and the cost of misoperation can be significant. It is better suited to automation scenarios with clear boundaries and relatively fixed workflows.

3. Route Two: Multi-Agent Collaboration + Local Code Execution

Core Architecture

The other route does not try to “simulate human operation of the interface.” Instead, it makes the agent directly oriented toward development scenarios: AI writes code, and the code is executed in the user’s local runtime environment. To handle complex tasks, such systems often adopt a multi-agent collaboration architecture — assigning a large task to agents with different roles, such as a planner responsible for decomposing requirements, an implementer responsible for writing code, a tester responsible for verification, and adversarial roles responsible for mutual review.

The significance of this division is to use “role-based checks and balances” to improve output quality. Code written by one agent can be reviewed or verified by another agent, reducing single-point errors.

The Value of Local Execution

“Local execution” is the key to this route. Code does not run in a remote sandbox; instead, it runs directly in your development environment. This means it can truly call your local dependencies, read real data, and operate on real project directories. For developers, this significantly shortens the “generate — run — verify” loop.

# Example: local execution loop driven by an AI agent (pseudocode)
task = parse_requirement("Add unit test coverage reporting to the project")
plan = planner_agent(task)          # Decompose the task
for step in plan:
    diff = coder_agent(step)        # Generate code changes
    apply_to_workspace(diff)        # Apply changes to the local workspace
    result = run_local_tests()      # Run tests locally
    if result.failed:
        step = reviewer_agent(result)  # Revise based on failure feedback

Capability Boundaries and Limitations

Its advantages lie in depth and accuracy. For code-related tasks, local execution can obtain real feedback from compilation, testing, and runtime, forming a reliable loop. It is suitable for scenarios that can run continuously for a relatively long time and complete complex development tasks.

However, the barrier is also higher. Users need some programming foundation and must understand project structure, dependency management, and version control. In addition, letting AI execute code locally is a double-edged sword, and permission boundaries must be strictly controlled.

4. Comparison Framework for the Two Routes

Dimension	Visual Control (Computer Use)	Multi-Agent + Local Code Execution
Core mechanism	Understands the screen and simulates mouse and keyboard input	Writes code and executes it locally
Suitable scenarios	Software without APIs, cross-system workflows	Coding, testing, and engineering tasks
Generality	High (anything with a GUI can be operated)	Medium (focused on development scenarios)
Stability	Easily affected by interface changes	More reliable due to real runtime feedback
Learning curve	Requires understanding the control concept	Requires programming foundation
Main risk	Misoperation in a real environment	Security boundaries of local execution

5. How to Choose: Decision by Scenario

Choosing a route should not be based on “which one looks more impressive,” but on the nature of the task:

• If the task target is GUI software without an API: prioritize the visual control route. It fills the automation gap that interfaces cannot cover.
• If the task is essentially writing code, running tests, and modifying projects: prioritize the local code execution route, because it provides a real feedback loop.
• If both types of needs exist: they can be combined. Use the code execution route for engineering work, and use the visual control route for legacy systems that can only be handled through GUI interactions.

6. Practical Recommendations: Security, Permissions, and Observability

Regardless of which route you choose, placing an agent in a real environment means taking on risk. There are three principles that must be maintained during deployment:

1. Least privilege: Grant only the file, directory, and network access permissions required to complete the task, avoiding giving the agent destructive capabilities beyond what is necessary.
2. Observability and traceability: All operations, especially execution-type operations, should be recorded to support later auditing. General capabilities such as logging services and monitoring alerts can be used to record and alert on the agent’s behavior chain.
3. Human confirmation at critical points: For irreversible operations such as deletion, publishing, or sending data externally, a human confirmation step should be set up to avoid consequences that are difficult to reverse.

Conclusion

Asking “which route is better” is actually a false question. Visual control and local code execution solve different dimensions of the problem. The former expands “what can be operated,” while the latter digs deeper into “how complex a professional task can be completed.” A truly mature practice is to understand the principles and trade-offs of each route, make choices according to specific scenarios, and maintain a baseline of security and observability. When you can clearly answer whether your task requires perceiving the interface or executing locally, the choice naturally becomes clear.