Parallelization
Run independent LLM calls concurrently and combine results.
Intent & Description
🎯 Intent
Run independent LLM calls concurrently and combine results.
📋 Context
A task either splits cleanly into independent subtasks that can run side by side — for example reviewing a pull request for security, style, and test coverage — or benefits from running the same prompt several times and combining the results, which is the basis of self-consistency style voting in mathematical reasoning. In both cases the agent is making more than one LLM call where none of the calls depend on each other’s output. The provider’s rate limits and the team’s budget can absorb running these calls in parallel.
💡 Solution
Two flavours. Sectioning: split a task into independent subtasks, run them concurrently, concatenate results. Voting: run the same task multiple times, aggregate by majority or judge.
Real-world Use Case
- Independent subtasks can run concurrently to cut wall-clock time.
- Voting across multiple attempts catches outliers a single run would miss.
- Aggregation by concatenation, majority, or judge is feasible.
Source
Advantages
- Wall-clock latency drops; quality rises (voting).
- Independent failures isolate cleanly.
Disadvantages
- Cost scales with branch count.
- Aggregation logic is its own correctness problem.