parallel-execution

Token overhead in parallel execution is reduced by batching independent tool calls into a single API message. This approach groups non-dependent operations like file reads and searches together, minimizing the context rebuilding costs associated with sequential processing.

Batching independent tool calls optimizes workflows by executing multiple non-dependent operations within a single cycle. This parallel investigation method prevents repeated context rebuilding, significantly cutting both latency and token costs compared to sequential calls.

Use parallel execution for scatter-gather research or bug investigation when you need to simultaneously read log files, search code snippets, and check external documentation. It processes multiple non-dependent verification operations efficiently in one go.

The best way to run multiple file reading and search tasks simultaneously is grouping them into one API call. By batching independent operations like reading multiple files together, you eliminate sequential latency and maximize agent efficiency.

The primary limitation of parallel execution is task dependency; it only applies to non-dependent operations. If tasks require sequential logic or rely on the output of previous calls, batching them into a single message is not possible.