Parallelization Pattern: Running Agent Tasks Concurrently
The Parallelization pattern executes independent sub-tasks concurrently rather than sequentially — reducing overall task completion time when sub-tasks don't depend on each other's results.
What Is the Parallelization Pattern?
The Parallelization pattern executes independent sub-tasks concurrently rather than sequentially. When an agent needs to gather information from three different systems, it doesn't have to wait for system A to respond before querying system B. All three queries run simultaneously and the agent reasons over the combined results.
When to Use It
- Multiple data lookups are needed and they don't depend on each other's results
- A task can be broken into independent sub-tasks that can be processed simultaneously
- Latency is a concern and the sub-tasks are individually time-consuming
When NOT to Use It
- Sub-tasks are sequential by nature — Step B requires Step A's output
- The overhead of coordination exceeds the time saved by parallelism (this is rare for I/O bound tasks)
- Parallel tool calls would create conflicting writes to the same system
How It Works in Workato
Workato's orchestration engine supports parallel execution of recipe branches. In the agentic context, the orchestrator dispatches multiple tool calls simultaneously and waits for all to complete before proceeding. The MCP protocol supports concurrent tool calls — the agent sends multiple tool requests without waiting for each to complete before sending the next.