dispatching-parallel-agents

To run parallel AI agents for task automation, you assign 2+ independent problems to isolated agents. This pattern preserves isolation by enforcing no shared context between agents, ensuring safe and reproducible results.

Parallel agent dispatching applies to unrelated tasks that do not share state, such as concurrent test fixes, independent code cleanups, or parallel research tasks. Each task must have a clear goal and strict scoping.

To ensure safe results with concurrent agents, you impose strict task scoping and enforce no shared context between them. Defining expected outputs for each isolated agent ensures reproducible results.

You should avoid parallel agents when tasks depend on each other or share state. This pattern is strictly for independent problems; applying it to dependent workflows breaks isolation and compromises safe execution.

No, concurrent AI agents require no shared memory or state configuration. The dispatching pattern explicitly enforces isolation between agents to guarantee reproducible and safe results.