Parallel agents are multiple AI agents or model calls working on independent parts of a task at the same time. The pattern reduces elapsed time when work can be separated cleanly, but it adds coordination, shared-state conflicts, duplicate actions, and a final step that must reconcile competing results.
An orchestrator worker pattern splits work into independent units, gives each worker isolated context, then combines the outputs. Multi-agent systems need explicit ownership of shared files and tools. Parallelism helps only when the work is actually independent.
When does parallel agents matter?
Use parallel agents for separate research questions, test suites, or document sections. Keep writes serialized or partitioned, and model coordination through an agent state machine. Two fast agents editing the same state can be slower than one.
Frequently asked questions
What is parallel agents used for?
Use parallel agents for separate research questions, test suites, or document sections. Keep writes serialized or partitioned, and model coordination through an agent state machine.
Are parallel agents always faster?
No. Coordination, duplicate work, and merge conflicts can cost more than the time saved.
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