Let the Agent Write While You Review: A Two-Track Workflow

The traditional rhythm of software development often feels like a series of stop and start motions. You write a function, you test it, you fix a bug, and you move to the next task. However, the introduction of large language models and autonomous assistants has introduced a new possibility: the ability to let the agent write while you review code in real time. This two track workflow shifts the developer role from a sequential producer to a parallel supervisor. By pipelining tasks, you can maintain high level architectural oversight while the heavy lifting of syntax and boilerplate happens in the background.

The Concept of a Pipelined Workflow

In computer architecture, pipelining allows multiple instructions to be processed at different stages simultaneously. A developer workflow can mirror this logic. Instead of waiting for an AI agent to finish a 200 line refactor before you even look at the first file, you can begin the review process as the output is generated. This requires a workspace that supports non modal interactions, where the activity of a tool does not lock the user interface.

Effective pipelining relies on three specific pillars. First, you need visibility into the agent process. Second, you need the ability to intervene without killing the session. Third, you need a spatial environment where the code being written and the code being reviewed are visible at the same time. When these conditions are met, the latency between creation and verification drops significantly.

Breaking the Sequential Bottleneck

Most integrated development environments are designed around a single active cursor. When an AI plugin starts writing, the screen often jumps or the editor locks up to prevent write conflicts. This forces you into a passive state. You become a spectator of your own IDE. To break this bottleneck, you must separate the writing stream from the review stream.

In a two track system, the agent occupies one lane. It might be generating unit tests for a newly created API endpoint or migrating a set of CSS files to a utility first framework. While this happens, you occupy the second lane. You can be checking the logic of the first five tests the agent already completed. By the time the agent reaches the end of the file, you have already approved the beginning.

Implementing Parallelism with Deska

Deska provides a unique environment for this style of work through its infinite canvas workspace. Unlike traditional IDEs that stack tabs on top of each other, Deska allows you to place panels anywhere. You can watch an agent work in one panel while you perform manual edits in another.

The platform runs multiple AI coding agents like Claude Code, Codex CLI, and OpenCode side by side as independent panels. This capability is central to the two track workflow. You can initiate a complex command in one agent panel and immediately move your focus to a different part of the canvas. Because Deska is local-first, your files and sessions stay on your machine, ensuring that the high speed exchange of data between the agent and your file system happens with minimal lag.

Using the Canvas for Oversight

The canvas allows you to zoom out to see the entire project structure. This architectural view is vital when an agent is touching multiple files. You can see the terminal output where the agent is running linting commands, the code editor where the changes are appearing, and a browser panel showing the live reload of the application. The spatial layout helps you maintain context without having to remember which tab holds which information.

Voice and System Control

If you need to adjust the environment while your hands are busy reviewing code, you can use Ask Deska. This is a voice and chat assistant that can drive the workspace for you. You can ask it to open new panels, run shell commands, or check the status of active sessions. This reduces the cognitive load of window management, allowing you to stay focused on the code logic.

Best Practices for Concurrent Review

Working alongside an active agent requires a disciplined approach to prevent merge conflicts and logical errors. Consider the following strategies:

• Small Scopes: Don't ask an agent to rewrite the entire backend in one go. Give it specific, granular tasks that result in readable chunks of code.
• Continuous Validation: As the agent completes a block, run your local test suite. If you are using Deska, you can keep terminals open right next to the code editor to see the test results immediately.
• Shared Context: Ensure the agent has access to your style guide or existing documentation. This prevents you from having to fix repetitive stylistic errors during your review track.
• Direct Pairing: If you need to step away from your desk but the agent is still working, you can use the mobile app. It pairs directly with your desktop through a secure relay to let you monitor progress from your phone.

The Role of Local Context

A major advantage of this workflow is the security and speed of local execution. When you use your own API keys via the BYOK model, you maintain control over your costs and data. The coding agents interact with your local file system directly. This means the review you are performing is on the actual files that will be committed to your repository. There is no middle ground or cloud staging area that might become out of sync with your local environment.

Comparison of Development Environments

Different tools take different approaches to AI integration. Some focus on autocomplete, where the AI predicts the next few characters. Others focus on chat interfaces where you copy and paste code back and forth. The pipelined workflow differs in approach by treating the agent as a background process that interacts with the filesystem while you operate in the foreground.

Traditional IDEs excel at deep static analysis and integrated debugging. However, they can sometimes feel crowded when multiple AI streams are active. A canvas based approach provides more "air" for the developer to organize these streams visually. This is not about replacing the IDE but rather extending the workspace to handle the increased throughput that AI enables.

FAQ

How to use AI coding agents for large refactors?

To handle large refactors, break the task into logical modules. Use one panel to track the overall progress and separate panels for each module. In Deska, you can use agent threads to keep different conversations organized. Review the changes module by module rather than waiting for the entire project to be altered, which prevents large scale errors from propagating.

What is the best way to sync AI code to mobile?

Monitoring your work on the move requires a secure connection between your primary machine and your mobile device. Deska uses a secure relay for its mobile app that does not require exposing ports on your router. This allows you to view the browser widgets or terminal output from a phone, ensuring the two track workflow continues even when you are not at your desk.

Is it safe to run AI agents on local code?

Security is a primary concern when giving agents access to your files. A local-first approach is generally safer because your source code does not live on a third party server. By using your own API keys, you control exactly which models have access to your data. Always check the privacy documentation of your tools to understand how your data is handled during inference.

Start Building Faster

Transitioning to a parallel workflow can significantly decrease the time it takes to move from an empty file to a production ready feature. By letting the agent handle the implementation details while you focus on the review and architecture, you maximize your most valuable resource: your time. You can experiment with this pipelined approach by downloading the Deska app for Mac, Windows, or Linux. The workspace itself is free, allowing you to arrange your terminals, editors, and agents in the way that best fits your project. Visit /download to get started and see how a spatial canvas can change your development speed.