Parallelization: Run Subtasks at Once, Then Merge

#ai #agents #llm #workflows

Short version: Parallelization runs independent subtasks at the same time and aggregates their outputs. Anthropic splits it into sectioning and voting, and Google calls it the parallel fan-out and gather pattern. Use it for speed, or to get several perspectives on the same problem for higher confidence.

What is parallelization?

Parallelization has LLMs work simultaneously on a task and then aggregates their outputs programmatically (Anthropic). Google's parallel pattern runs multiple specialized subagents on a task or subtasks at the same time, then synthesizes their outputs into one consolidated response (Google Cloud).

Anthropic names two variations (Anthropic):

Sectioning: break a task into independent subtasks and run them in parallel.
Voting: run the same task several times to get diverse outputs, then combine them.

How it actually works

A dispatcher fans the work out, the workers run concurrently, and a final synthesizer gathers the results. Google's canonical example is automated code review: spawn a security auditor, a style enforcer, and a performance analyst on a pull request at the same time, then have a synthesizer combine their feedback into one review comment (Google Developers).

One warning from Google's ADK guide: parallel agents run in separate threads but share session state, so each agent must write to a unique key to avoid race conditions (Google Developers).

# Google ADK, sketch
sec  = LlmAgent(name="Security", output_key="security_report")
sty  = LlmAgent(name="Style", output_key="style_report")
perf = LlmAgent(name="Performance", output_key="performance_report")
swarm = ParallelAgent(sub_agents=[sec, sty, perf])
merge = LlmAgent(name="Synthesizer",
                 instruction="Combine {security_report}, {style_report}, {performance_report}.")
workflow = SequentialAgent(sub_agents=[swarm, merge])

When to use it

Use parallelization when the subtasks are independent and can run at once for speed, or when several attempts raise your confidence in the result (Anthropic). For complex tasks with many considerations, models often do better when each consideration gets its own focused call rather than one prompt trying to hold all of them.

Two Anthropic examples make the split concrete. Sectioning fits guardrails: one model handles the user query while another screens it for problems, which beats making one call do both. Voting fits code review for vulnerabilities: several prompts each look for a problem, and you flag the code if any of them find one (Anthropic).

When not to use it

Do not parallelize when the subtasks depend on each other. If step two needs step one's output, they are a chain, not a fan-out. Do not use it when the subtasks are decided at runtime rather than fixed. That is orchestrator-workers, which looks similar but lets the lead agent choose the subtasks. And if a single call is fast and good enough, running many calls just multiplies cost.

Known problems

The clearest cost is exactly that: running multiple agents at once raises immediate resource use and token consumption, which drives up operational cost (Google Cloud). You trade money for latency.

The harder problem is the gather step. Google notes that synthesizing potentially conflicting results requires complex logic, which adds to development and maintenance overhead (Google Cloud). When two workers disagree, someone has to decide who wins, and that logic is where parallel systems get messy. Add the shared-state race condition risk, and careful key management becomes non-negotiable.

Three things to know before you start

Give every worker a unique output key. Shared state plus concurrent writes equals race conditions. Distinct keys prevent them.
Design the merge first. The synthesizer is the hard part, not the fan-out. Decide up front how you resolve conflicts.
Know your variant. Sectioning splits different subtasks, and voting repeats the same task for confidence. They solve different problems.

FAQ

What is the difference between sectioning and voting?
Sectioning breaks a task into different independent subtasks that run in parallel. Voting runs the same task multiple times and combines the outputs to raise confidence.

Is parallelization the same as orchestrator-workers?
They look alike, but parallelization uses fixed, predefined subtasks, while orchestrator-workers decides the subtasks at runtime. Fixed versus dynamic is the dividing line.

Does parallelization always cost more?
It uses more tokens and compute at once, so it usually costs more per request. You accept that to gain speed or higher confidence.

How do I combine conflicting outputs?
That is the gather step, and it needs deliberate logic: a vote threshold, a priority order, or a synthesizer prompt. Plan it before you build the fan-out.