Faster Workflow Processing
Workflows are automation — which means their performance matters most at scale. A workflow that runs once a day can afford to take a few seconds. A workflow that processes hundreds of records per hour needs to be fast.
This update brings a 3x improvement in workflow execution speed across the board.
Where the Time Was Going
A workflow step has several layers: parsing the step definition, loading record data, evaluating conditions, executing the action, saving the result. Each layer adds time, and layers that could be parallelized were running sequentially.
The optimization targeted three areas:
Definition caching. Workflow definitions were being parsed on every execution. They're now cached on first load and invalidated only when the definition changes. The parsing overhead — which could represent 30-40% of step execution time for complex workflows — drops to near zero for repeat executions.
Parallel step evaluation. Workflow steps that don't have data dependencies on each other now execute in parallel where possible. A step that sends an email and a step that updates a related record can run simultaneously rather than sequentially.
Batch record loading. Steps that reference related records now load those records in a single query rather than individual per-record queries. A workflow step that accesses five related records no longer makes five separate database calls.
The Impact
For workflows processing high volumes — overnight batch processes, event-triggered automation that runs frequently — the 3x improvement translates directly to: processes that took an hour now take 20 minutes, throughput that topped out at a certain rate can now handle three times as many events in the same window.
No Configuration Changes
The optimization is transparent. Existing workflows run faster without any modification. The improvement applies across all workflow types — manual triggers, event-based triggers, scheduled execution.
Fast automation is reliable automation. Processes that run quickly leave less window for failures, timeouts, and the edge cases that compound in slower systems.