dispatching-parallel-agents

Parallel multi-agent execution requires splitting tasks into independent subquestions and verifying no data dependencies, shared file writes, or shared intermediate state before concurrent assignment. This prevents wasted time and corrupted outputs.

Pipeline orchestration for independent question subtasks involves assigning pipeline-produced subquestions to multiple subagents concurrently. It enforces hard independence checks to ensure no dataflow dependencies or shared intermediate state exist before execution.

Checking data dependencies before dispatching parallel agents involves verifying no dataflow dependencies, no shared file writes, and no need for shared intermediate state. If any independence rule fails, execution must switch to sequential or batch mode.

You should avoid parallel agent dispatch and use sequential execution when independence checks fail, meaning subtasks share data dependencies, write to the same files, or require shared intermediate state. This fallback prevents corrupted outputs and conflicts.

Running parallel文献 queries with multi-agent workflows is possible if you enforce no shared file writes and no shared intermediate state. The workflow must verify task independence before concurrent assignment to prevent file conflicts.