The Deska blog
Parallel AI Agents: Running Several Claude Code Instances on One Project
Learn how to orchestrate parallel AI agents like Claude Code using spatial canvases to improve developer productivity and manage complex architectural changes.
· 10 min read
The emergence of autonomous command line tools has shifted the focus from simple code completion to agentic workflows. When you deploy parallel AI agents like Claude Code on a single project, you move beyond the limitations of a single conversational thread. This approach allows a developer to delegate multiple subtasks simultaneously, such as refactoring a backend module while another agent updates the corresponding frontend components. Managing these concurrent streams requires a rigorous strategy for synchronization and a workspace that provides high visibility into every active process.
The Logic of Parallel AI Agents
Standard AI coding assistants usually operate within a single context window. This creates a bottleneck where the agent must finish one task before starting the next. By contrast, running parallel AI agents allows for a divide and conquer strategy. This is especially useful in large monorepos or projects with microservices where changes in one area do not immediately break another.
There are three primary reasons to run multiple instances of an agent like Claude Code at once:
- Task Isolation: You can isolate a risky experimental feature in one instance while maintaining a stable refactor in another.
- Context Preservation: Each agent maintains its own short term memory and command history, preventing the "forgetfulness" that occurs when a single chat becomes too long.
- Speed: Instead of waiting for an agent to index files and propose a large diff, you can have three agents working on three smaller diffs.
Orchestration Patterns for Concurrent Workflows
Running multiple agents is not just about opening multiple terminals. Without a plan, you will encounter merge conflicts and hallucinated dependencies. Effective orchestration usually follows one of several patterns.
The Supervisor Pattern
In this model, the human developer acts as the central router. You assign a specific scope to each agent instance. For example, Instance A is restricted to the /src/api directory, while Instance B focuses on /src/hooks. You review the output of each before they are committed to the main branch. This prevents agents from overwriting each other's work.
The Pipeline Pattern
This involves a handoff. One agent might be responsible for generating unit tests based on existing code, while a second agent works on the implementation to make those tests pass. By running them in parallel, you can begin inspecting the test suite while the implementation is still being drafted.
The Reviewer Pattern
You can dedicate one agent instance to purely analytical tasks. While Instance A writes code, Instance B is tasked with reading the modified files and looking for security vulnerabilities or style inconsistencies. This creates a real time feedback loop that mirrors a human pair programming session more closely than a single agent ever could.
Managing the Cognitive Load with a Spatial Canvas
The biggest challenge with parallel AI agents is the interface. Standard IDEs hide terminals in tabs or small bottom panels. When you have four Claude Code sessions running, flipping between tabs leads to context switching fatigue. This is where a spatial canvas becomes essential.
By using an infinite canvas, you can place each agent session in its own dedicated panel. This allows you to see the logs of the backend agent while simultaneously watching the frontend agent update the UI. You can zoom out to see the high level progress of all agents or zoom in on a specific terminal when it requires manual intervention.
Deska provides this environment through its canvas interface. Instead of a rigid grid, you can arrange panels in clusters based on the feature you are building. You might have a code editor panel in the center, flanked by two Claude Code terminals and a browser widget to preview the changes. This layout makes the relationship between the agents and the codebase visible at a glance.
Technical Practicalities: Locks and File Systems
When multiple agents write to the same file system, integrity is a concern. Claude Code and similar tools like OpenCode are aware of the file state, but they might not always know what another agent is doing in real time.
| Strategy | Benefit | Risk |
|---|---|---|
| Branch Isolation | No direct conflicts | Requires complex merging later |
| Directory Scoping | High speed and parallel flow | Missed cross directory dependencies |
| Shared Worktree | Live visibility | Potential for file corruption if agents write simultaneously |
To mitigate these risks, it is best to use a local-first approach where you can quickly revert changes using Git. Tools that prioritize local-first data handling ensure that even if two agents create a conflict, your source of truth remains on your machine and under your control.
Leveraging Deska for Agent Multi-tenancy
The Deska architecture is built specifically to host multiple agents side by side. Because Deska runs as a desktop app on Mac, Windows, and Linux, it handles the resource management of having several CLI tools active at once.
You can use the terminals in Deska to launch several instances of Claude Code, Codex CLI, or OpenCode. Each panel is an independent session. If one agent gets stuck in a loop, it does not freeze the rest of your workspace.
For further efficiency, the Ask Deska assistant can help you organize these sessions. You can use voice or chat to tell Deska to "open three terminals and start Claude Code in each." This reduces the manual setup time required to initialize a parallel workflow. If you need to step away from your desk, the mobile app allows you to monitor the output of these long running parallel tasks through a secure relay.
FAQ
How do I prevent parallel AI agents from conflicting?
The most effective way is to assign each agent to a different subdirectory or a different Git branch. By scoping their file system access, you minimize the chance of two agents editing the same line of code simultaneously.
Does running multiple Claude Code instances cost more?
It depends on your billing model. If you use your own API keys, you are charged based on the total tokens used across all instances. Running agents in parallel doesn't inherently cost more than running them sequentially, but it allows you to consume tokens faster.
Can I run different types of agents together?
Yes. A powerful pattern is to use Claude Code for its reasoning capabilities and a specialized tool like OpenCode for boilerplate generation. Using coding agents with different strengths in parallel allows you to use the best tool for each specific subtask.
Getting Started with Parallel Workflows
To implement these patterns, you need an environment that does not constrain your view. Traditional tab-based editors are often too small for the level of multitasking required by agentic development.
Moving your workflow to a spatial workspace allows you to treat AI agents as a team of collaborators rather than a single script. You can arrange your tools, see the connections, and maintain control over the entire process. Download the Deska app at /download to start building your own multi-agent workspace for free on your local machine.