Parallel development with coding workers

Parallel development with SwarmForge means assigning independent software-engineering tasks to separate coding workers, allowing them to run concurrently while a manager reviews and integrates their outputs.

Choose work that can proceed independently

For a checkout feature, a manager might assign one worker to a defined API change, another to inspect existing payment edge cases, and a third to document the current interfaces. These are example task scopes, not published performance results.

Tasks that depend on an unfinished API should wait for its agreed contract or branch handoff. Separate VMs do not automatically prevent workers from proposing conflicting source changes. Limit shared-file edits and make the base revision explicit in the instructions.

Delegate concrete deliverables

Use spawn_worker with a common task ID and distinct instructions or role labels. Select a built-in code, review or documentation template where useful. Include expected files, constraints and what evidence should accompany the result.

Give each creation request its own stable request ID and use that same ID only when retrying that request. Observe each worker independently; a team-level count alone does not prove the whole feature is ready.

Review and integrate

Inspect persisted results, changed files, test reports, preserved artifacts and configured Git branch handoffs. Verify tests independently before merging. SwarmForge records worker reports and can verify branch pushes; the manager decides whether the result meets the task.

Integration, conflict resolution and pull-request creation remain external workflow steps. Use follow-up messages to request bounded corrections on a retained worker session when appropriate.

Finish without leaving retained infrastructure

Collect deliverables and verify source handoffs before normal cleanup. Completed, paused and failed VMs can consume capacity and provider resources. Preview retention eligibility, inspect refusals and destroy only eligible environments.

Parallelism is a capability, not a claim that every task becomes faster or cheaper. VM startup, model capacity, dependency order, review and interventions must be measured for the actual workload.