You are developing a complex workflow by using Azure Durable Functions. During testing you observe that the results of the workflow differ based on how many instances of the Azure Function are…

Durable Functions orchestrator functions must always be deterministic, meaning they produce the same output given the same input, irrespective of execution instance or replay. Non-deterministic operations, like generating random numbers or getting current time directly, can cause inconsistent results across different function instances.

While reading state data from the durable function context is correct practice, it does not inherently resolve non-deterministic logic within the orchestrator function itself.

Configuring the function to run on a single App Service Plan instance might mask the problem but does not address the underlying non-deterministic nature of the orchestrator code, which is a fundamental design requirement.

Implementing the monitor pattern is a specific Durable Functions pattern, but it is not a solution for non-deterministic orchestrator code causing inconsistent results across instances.