Execute multiple independent tasks simultaneously to improve efficiency and reduce latency
Execute multiple independent tasks simultaneously to dramatically reduce processing time and improve system efficiency
Many agentic workflows involve multiple sub-tasks that must be completed to achieve a final goal. A purely sequential execution, where each task waits for the previous one to finish, is often inefficient and slow. This latency becomes a significant bottleneck when tasks depend on external I/O operations, such as calling different APIs or querying multiple databases. Without a mechanism for concurrent execution, the total processing time is the sum of all individual task durations, hindering the system's overall performance and responsiveness.
The Parallelization pattern provides a standardized solution by enabling the simultaneous execution of independent tasks. It works by identifying components of a workflow, like tool usages or LLM calls, that do not rely on each other's immediate outputs. Agentic frameworks like LangChain and the Google ADK provide built-in constructs to define and manage these concurrent operations. By running these independent tasks at the same time rather than one after another, this pattern drastically reduces the total execution time.
Use this pattern when a workflow contains multiple independent operations that can run simultaneously, such as fetching data from several APIs, processing different chunks of data, or generating multiple pieces of content for later synthesis.
Parallel execution diagram showing multiple tasks running simultaneously
Topic: parallelization
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Imagine you're running a busy restaurant kitchen. You have multiple dishes to prepare for a large order. You could have one chef prepare each dish one at a time (sequential), or you could have multiple chefs working on different dishes simultaneously (parallel). The parallel approach gets the entire order done much faster!
In AI agent systems, parallelization means executing multiple independent tasks at the same time instead of waiting for each one to finish before starting the next. This is especially powerful when:
Multiple tasks start and run at the same time, rather than waiting in a queue. The total time is determined by the slowest task, not the sum of all tasks.
Fan-Out: Split work into multiple parallel branches
Fan-In: Collect and combine results from all branches
Distribute tasks to multiple agents (scatter), then gather and synthesize their results into a final answer.
Note that asyncio provides concurrency, not true parallelism. It achieves this on a single thread by using an event loop that intelligently switches between tasks when one is idle (e.g., waiting for a network request). This creates the effect of multiple tasks progressing at once, but the code itself is still being executed by only one thread, constrained by Python's Global Interpreter Lock (GIL).
The adoption of a concurrent or parallel architecture introduces substantial complexity and cost, impacting key development phases such as design, debugging, and system logging.
Parallel workflows are harder to design, understand, and maintain. Debugging becomes more challenging as you need to track multiple execution paths simultaneously.
Running multiple LLM calls or API requests simultaneously increases your API usage and costs. Make sure the performance gain justifies the additional expense.
When multiple tasks fail, it's harder to identify which one caused the issue and why. Proper logging and error handling become critical.
Be mindful of rate limits on external APIs and services. Too many parallel requests might hit rate limits or overwhelm downstream systems.
