Orcas Workspace: Running Coding Agents Side by Side

Orcas Workspace lets you run multiple coding agents side by side in a single, unified environment, each with its own isolated workspace and shared project context. You enter a task or prompt, and the platform spins up independent agent sessions that work concurrently on the same codebase, with results merged or reviewed in a central interface. This article explains how the side-by-side model works, what it is built for, and how it differs from running agents sequentially or in a single chat thread.

What the Side-by-Side Model Actually Means

In a typical coding-agent setup, you paste a prompt into a chat window, the agent edits files, and you review the diff. Orcas Workspace changes the default: instead of one agent doing one thing at a time, you can launch several agents at once, each with its own task, its own file-access permissions, and its own conversation history. The key word is side by side—the agents run in parallel, not in a queue.

Concretely, that means you might have one agent refactor a database migration, a second agent update the API documentation, and a third agent fix a failing test suite—all at the same time, all in the same repository. Each agent sees the same project snapshot, but its edits are isolated until you choose to merge them. The platform tracks which files each agent touched, so you can review changes per agent, per file, or as a combined diff.

How Orcas Workspace Organizes Parallel Work

Under the hood, Orcas Workspace creates a separate container or virtual workspace for each agent session. That isolation is what makes parallelism safe: if one agent deletes a file or runs a destructive command, the others are not affected. The platform then layers a shared file system on top, so all agents read the same initial codebase. When an agent finishes, its changes are presented as a patch that you can apply, reject, or edit.

The interface shows a dashboard with each agent’s status (running, waiting, done, error), its current step, and a log of its actions. You can open any agent’s diff, leave comments, or send a follow-up message that only that agent sees. There is also a global chat that broadcasts to all agents—useful for mid-task corrections like “stop touching the config file� or “use the new API endpoint.�

Why Run Agents in Parallel Instead of Sequentially?

The main benefit is wall-clock time. If you have three independent tasks that each take ten minutes, doing them one after another takes thirty minutes; running them side by side takes roughly ten. That speed matters when you are iterating on a large feature or trying to fix multiple bugs before a deadline.

Parallelism also encourages decomposition. Instead of asking one agent to “build the login flow,� you can split it into “write the backend auth endpoints,� “create the frontend form,� and “write integration tests.� Each agent can focus, and you get three smaller diffs that are easier to review than one giant change. The trade-off is coordination overhead: you need to define clear boundaries so agents do not edit the same files or depend on each other’s unfinished work.

Choosing Tasks That Suit Side-by-Side Execution

Not every task benefits from parallelism. Good candidates are:

Independent features that touch different modules or directories
Repetitive changes like updating imports, renaming variables, or adding logging
Documentation updates alongside code changes
Test-writing for a stable API while another agent refactors internal code
Code review or linting tasks that do not modify files

Poor candidates are tasks that modify the same files, require strict ordering (e.g., migration then model then endpoint), or depend on a shared resource like a database schema. If two agents both edit utils.js, you will get merge conflicts that defeat the purpose. A good rule of thumb: if you can describe the tasks as “A and B� rather than “A then B,� side-by-side works.

Common Problems and How to Avoid Them

The most frequent issue is merge conflicts. Because agents work on the same repository, their changes can overlap. Orcas Workspace tries to detect conflicts early—it shows a warning when two agents have edited the same file—but it cannot resolve semantic conflicts (e.g., one agent renames a function another agent still calls). To reduce this, assign file ownership explicitly in your prompts: “only touch files in src/api� or “do not modify package.json.�

Another problem is context drift. Each agent has its own conversation, so it does not know what the others are doing. If you need them to coordinate, use the global broadcast or write a shared spec file that all agents read. Finally, resource limits: running five agents at once consumes more CPU and memory than one. If your machine or the cloud plan is small, you may see slowdowns. The platform lets you cap the number of concurrent agents, so you can balance speed and stability.

Comparing Orcas Workspace to Other Agent Tools

Most coding agents, such as GitHub Copilot Workspace or Cursor’s agent mode, operate in a single thread: you chat, it edits, you review. Orcas Workspace’s differentiator is the explicit parallel model. Instead of you manually opening multiple chat windows and copying diffs, the platform manages isolation, merging, and cross-agent communication for you.

Other tools like Aider or OpenHands also support multiple sessions, but they are typically separate processes that share a filesystem without a unified review interface. Orcas Workspace is closer to a project-management layer on top of agent runtimes: it gives you a board-like view of all agents, their outputs, and their conflicts. For teams, that central dashboard is the main advantage—you can see at a glance what each agent did and approve changes with a few clicks.

Costs, Limits, and Safety Considerations

Running agents side by side multiplies compute usage, so costs scale with the number of concurrent sessions. If you are paying per token or per minute, five parallel agents cost roughly five times as much as one, though the wall-clock savings may justify it. Free tiers typically limit concurrency to one or two agents; paid plans allow more.

Safety is a real concern. Agents can run commands, modify files, and even push to git if you grant permissions. Orcas Workspace has guardrails: you can restrict an agent to read-only mode, block certain commands (like rm -rf or git push), and require manual approval before any change is applied to the main branch. Always review the diff before merging, and never give an agent write access to production secrets or credentials. The platform also keeps a full log of every action, so you can trace what happened if something breaks.

Frequently Asked Questions

Can two agents edit the same file without conflict?

Technically yes, but it is not recommended. Orcas Workspace will detect overlapping edits and flag them, but you will have to manually reconcile the changes. If you must have two agents touch the same file, give each a specific function or section to modify, and check the combined diff carefully.

How do I share context between agents?

Use the global broadcast message, which sends a note to every running agent. You can also create a SPEC.md file in the repo that all agents read as part of their instructions. For persistent shared state, commit a file that agents can read and update, but be aware that concurrent writes may cause races.

What happens if one agent fails?

Other agents continue running. The failed agent’s partial changes are kept in its workspace but not merged. You can inspect the log, fix the issue, and re-run that agent with a corrected prompt. The platform does not automatically roll back changes from other agents.

Does Orcas Workspace work with any language or framework?

It works with any codebase that can run in a container, since each agent operates on a generic file system. Language-specific tools (like linters or test runners) need to be installed in the workspace image, but the core model is language-agnostic.

Is it safe to let agents run git commands?

Only if you restrict them. By default, agents can run git commands, but you can disable that or require approval for destructive actions like git reset --hard or git push. For most workflows, let agents create branches and commit locally, but require a human to merge to the main branch.

How many agents can I run at once?

That depends on your plan and hardware. Free tiers usually cap at 2–3 concurrent agents; paid plans may allow 10 or more. The platform will warn you if you are hitting resource limits, and you can always reduce the number to keep things stable.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.

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