Parallel Execution

Sequential agents waiting on unrelated I/O waste time. Parallelism cuts a 30-second pipeline to 10 seconds when three retrievals can run simultaneously.

Parallel execution is a restaurant kitchen firing all appetizers at once instead of cooking dishes one at a time.

Scenario

User asks for weather in 5 cities — parallel tool calls to weather API, merge results in synthesis node.

Solution

In Multi-Agent Systems, apply Parallel Execution to this scenario: User asks for weather in 5 cities — parallel tool calls to weather API, merge results in synthesis node. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Parallel Execution (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Parallel Execution happens in the code.

1import asyncio  # import dependencies2
3async def parallel_research(queries):  # key line for Parallel Execution4    tasks = [research_agent.run_async(q) for q in queries]5    results = await asyncio.gather(*tasks, return_exceptions=True)6    return [r for r in results if not isinstance(r, Exception)]  # return the result

• Treating Parallel Execution as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for parallel execution

Quick recap — the most important points from this module.