Having an AI Agent Write Your Tests While You Write Features

The modern development cycle often forces a trade-off between velocity and quality, a friction point where having an AI agent write your tests while you write features can change the entire equation. Traditional unit testing usually follows the developer as an afterthought, leading to gaps in coverage or delayed deployments. By delegating the repetitive task of test suite construction to an autonomous agent, you maintain focus on business logic and architecture. This split-brain workflow is not about replacing human oversight, it is about parallelizing the effort of building robust software.

The Bottleneck of Manual Test Coverage

Writing tests is a fundamental part of reliable software engineering, yet it is often the first thing sacrificed when deadlines loom. Manual testing requires a significant context switch from the creative process of designing a feature to the defensive process of trying to break it. This switch consumes mental energy and slows down the feedback loop.

Software testing generally falls into several categories, each requiring different levels of effort:

• Unit testing for individual functions and logic.
• Integration testing to ensure different modules communicate correctly.
• End-to-end testing to verify the entire user journey.
• Regression testing to prevent old bugs from resurfacing.

AI agents are particularly adept at unit and integration testing because these tasks rely on understanding code patterns and anticipating edge cases. When you allow an agent to handle the boilerplate of descibe and it blocks, you free your mind to solve the complex architectural problems that require human intuition.

Implementing a Parallel Coding Workflow

To effectively have an AI agent write your tests while you focus on features, you need an environment that supports concurrency. Most standard IDEs are linear, you work on one file, save it, and perhaps run a test in a small hidden terminal. A parallel workflow requires you to see the feature code and the test suite simultaneously, interacting with both in real time.

This is where a specialized workspace becomes essential. By using an infinite canvas, you can place your code editor directly next to an agent terminal. As you define a new function, the agent observes the file changes and begins scaffolding the corresponding test file. This spatial arrangement reduces the cognitive load of switching tabs and keeps the entire project context visible.

Comparing Traditional IDEs and Canvas Workspaces

Leveraging Deska for Agent Managed Testing

Deska provides the infrastructure needed for this specific split-brain approach. Because Deska is a free desktop app for Mac, Windows and Linux, it serves as a local-first environment where your source code never leaves your machine unless you explicitly send it to an LLM provider.

Within the canvas interface, you can open a terminal panel running an agent like Claude Code or OpenCode. While you are typing code in the Monaco-based editor panel, the agent can be instructed to watch the directory and generate tests for every new exported member. You can zoom out to see the feature code, the test code, and the test runner output all at once.

If you are away from your workstation, you can use the mobile app to check the progress of these agent sessions. Since the mobile app uses a secure relay to pair directly with your desktop without exposing ports, you can monitor a long-running test generation suite while on the go.

The Role of Ask Deska in Workspace Management

Managing multiple panels, terminals, and agents manually can be distracting. Using ask-deska, the built-in voice and chat assistant, you can automate the setup of your environment. You might tell the assistant to open a new terminal, start a specific agent, and tile it next to your current editor.

This level of workspace control ensures that the transition between writing code and reviewing agent-generated tests is seamless. The assistant can also help you check the status of active sessions or run specific shell commands to verify that the generated tests are passing, all through natural language interfaces or voice commands.

Best Practices for AI Generated Tests

While having an agent write your tests is efficient, it requires a structured approach to ensure quality. You should not blindly accept every test produced by an AI.

1. Define clear interfaces: Agents perform better when your code has clear inputs and outputs.
2. Review for logic errors: AI is great at syntax but can sometimes hallucinate the intended behavior of a complex business rule.
3. Use a local-first approach: Keep your test data and sensitive logic on your machine.
4. Iterate quickly: Run the tests as soon as they are generated to catch misalignments between your feature code and the agent's understanding.

Security and Privacy in AI Workflows

One major concern when using AI for coding is the privacy of the codebase. Deska addresses this by being local-first. The app itself does not store your code on remote servers. For users who prefer complete control over their costs and data flow, the BYOK (Bring Your Own Key) model for the lifetime tier ensures that you only interact with the LLM providers you trust. Subscribers can also use managed inference if they prefer a more streamlined setup. More details on these options are available on the pricing page.

Frequently Asked Questions

Can AI agents write unit tests for existing legacy code?

Yes, AI agents are incredibly effective at analyzing existing codebases and creating regression tests for legacy modules. By running an agent in a terminal panel, you can point it to an undocumented file and have it generate a comprehensive test suite that documents the current behavior of the code, making future refactoring safer.

How do I keep my code private while using AI agents?

Privacy is maintained through a local-first architecture. The files remain on your disk, and only the specific code snippets required for the prompt are sent to the AI provider via your own API keys. Deska does not act as an intermediary for your data, ensuring that your intellectual property remains under your control.

Does the AI agent run the tests or just write them?

In a canvas-based environment, you can configure the agent to both write and execute the tests. The agent operates within a terminal panel that has access to your local shell, allowing it to run commands like npm test or pytest and then iterate on the code based on the failure logs it receives.

Optimize Your Workflow

The transition from manual testing to an agent-assisted workflow is a significant leap in developer experience. By utilizing a workspace that supports side-by-side collaboration between humans and AI, you can ensure that your test coverage grows at the same pace as your feature list.

You can download Deska today to start building your own infinite canvas workspace and explore how parallelized testing can improve your development speed. For more information on setting up your environment, visit the getting started guide.