Babysitting Agents vs Batching Their Work

The shift from manual coding to using autonomous assistants often introduces a new, hidden bottleneck where developers find themselves babysitting agents instead of performing high level architecture. This phenomenon occurs when a user triggers a single agentic process and watches the terminal output line by line, waiting for completion before starting the next task. While this ensures accuracy for small patches, it fails to leverage the true potential of non-linear development. Transitioning to a model of batching their work allows a developer to treat agents as parallel workers rather than fragile processes that require constant supervision.

The Cost of Sequential Supervision

When you interact with a coding agent in a standard single-pane CLI, you are effectively tied to its execution speed. This "babysitting" mode creates a cognitive load that mimics the very manual labor agents are meant to replace. If an agent takes three minutes to index a repository or run a test suite, those are three minutes of developer downtime.

Many developers justify this by claiming they need to catch errors early. However, the feedback loop of a single agent is often too slow for complex refactoring. If you have five unrelated components to update, running them one after another in a single thread is an inefficient use of compute and human time. The primary constraint becomes the human ability to wait for a prompt to finish.

Moving Toward Batching Workflows

Batching involves preparing multiple distinct tasks and letting several agents or threads handle them simultaneously. Instead of watching one agent solve one problem, you orchestrate a fleet of agents to address a backlog of technical debt or feature requests. This requires a shift in how we structure our workspace and our expectations of agent autonomy.

To move away from babysitting, a developer needs several capabilities:

• The ability to run multiple agents side by side without context bleed.
• A visual overview of all active processes to monitor progress at a glance.
• Reliable logging that captures the thought process of each agent for later review.
• A way to intervene only when a specific threshold of uncertainty is reached.

By treating agents as background workers, you can focus on the "what" while the agents handle the "how" across different branches or modules. This is the difference between being a solo coder and being a technical lead for a digital team.

Comparing Architectural Approaches

Different tools handle the agentic workflow in various ways. Some focus on deep integration within the editor, while others prioritize the isolation of the execution environment.

Standard CLI tools like Claude Code or OpenCode are powerful but often force a sequential workflow. Traditional IDE extensions are excellent for code completion but can become cluttered when managing three or four autonomous tasks. A canvas based approach allows for a more organized batching strategy where each task has its own dedicated physical space.

Orchestrating Parallelism with Deska

Deska provides an environment specifically designed to move away from babysitting agents. Because it uses an infinite canvas, you can place multiple terminals and agent panels anywhere in your workspace. You might have Claude Code refactoring an API layer in one corner while Codex CLI generates unit tests for a frontend component in another.

By zooming out, you can see the status of every running agent. You are not stuck staring at a single cursor. You are observing a system of work. This is made possible through the use of panels that can host different agents side by side.

If you need to check on a long running process while away from your desk, the mobile app allows you to monitor these sessions via a secure relay. This means "batching" can happen over longer periods. You can trigger a massive refactoring batch before a lunch break and monitor the progress from your phone, stepping in only if a terminal shows a build error.

The Role of Local-First Execution

Security and speed are critical when batching agent tasks. A local-first approach ensures that even when you have ten agents running, your source code and session data stay on your machine. This reduces the latency involved in cloud round trips and keeps your intellectual property secure.

When you use your own API keys via a BYOK model, you have full control over the costs associated with batching. You can choose to run cheaper models for documentation tasks while reserving high reasoning models for complex logic, all within the same workspace.

Managing Multiple Contexts

One challenge of batching is context fragmentation. If you are running multiple agents, you need to ensure they are not stepping on each others' toes. Using agent threads in Deska allows you to isolate specific conversations and file sets. You can manage these distinct work streams through the command palette or by using Ask Deska, the voice and chat assistant that can help you organize your canvas by opening panels or checking session status across the workspace.

Best Practices for Batching

1. Define Clear Boundaries: Assign each agent a specific, isolated task with clear entry and exit criteria.
2. Use Version Control: Always run agents on separate git branches. This allows you to review and merge their work in batches rather than dealing with a messy working directory.
3. Monitor via Telemetry: Instead of watching code being written, watch for the results. Successful test passes or linting completions are better indicators of progress than a moving cursor.
4. Document the Intent: Use the notes panel to track what each agent is supposed to be doing. This serves as a dashboard for your batched operations.

FAQ

How to run multiple agents in parallel?

To run agents in parallel, you should use a workspace that supports multiple independent terminal sessions or panels. You can open several terminal panels and initiate different agent processes in each, ensuring they operate on separate branches or directories to avoid write conflicts.

Is it safe to leave AI agents unattended?

It is safe if you use a local-first environment where the agent's actions are confined to your local file system and git state. You should always review the changes before committing them. Monitoring tools like a mobile relay can help you keep an eye on progress without being tethered to your computer.

What are the benefits of a canvas for coding?

A canvas provides a spatial way to organize work. Unlike tabbed interfaces, a canvas lets you see the code editor, terminal, and agent output all at once. This reduces the cognitive load of switching between tasks and makes it easier to manage several batched processes simultaneously.

Scaling Your Workflow

Moving from babysitting to batching is a necessary step for any developer looking to stay productive in an AI centric world. By using tools that support coding agents in a flexible, multi-tasking environment, you reclaim your time for architectural decisions and complex problem solving.

If you are ready to stop watching terminals and start orchestrating work, you can begin by setting up your own parallel workspace. Download Deska for Mac, Windows, or Linux and start building your fleet of agents today.