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When should an enterprise architect choose Cloud Flows versus Desktop Flows (PAD) in a modern automation roadmap?
Enterprise architects enforce an 'API-First, RPA-Last' doctrine: 1. Choose Cloud Flows (iPaaS) whenever target endpoints offer accessible REST/SOAP APIs, webhooks, or native Power Platform connectors (SharePoint, Dataverse, Salesforce, SAP OData, SQL). Cloud flows run entirely on Azure serverless microservices with 99.9% availability, zero VM maintenance, sub-second latency, and deterministic HTTP status codes. 2. Choose Desktop Flows (PAD) strictly as a last resort for legacy UI-bound applications (e.g. Windows Forms, SAP GUI, AS400 terminal emulators, Citrix virtual apps, or external portals lacking APIs) where a bot must emulate physical mouse clicks and keystrokes on a Windows VM session. Hybrid Architecture: Standard enterprise practice is to use a Cloud Flow as the intake/dispatcher engine (parsing emails, extracting attachments, queuing records) and delegate exclusively the legacy UI steps to an unattended Desktop Flow.
How do you implement the enterprise Scope Try-Catch-Finally error handling pattern in Cloud Flows, and extract specific failure root causes?
The enterprise pattern divides flow logic into three sequential Scope containers: 1. Scope_Try: Contains the primary business logic and connector actions. 2. Scope_Catch: Configured via 'Configure run after' to execute ONLY if Scope_Try 'has failed' or 'has timed out'. Inside Scope_Catch, execute a 'Filter array' action on `@result('Scope_Try')` with condition `@equals(item()?['status'], 'Failed')`. Extract the failed action name (`item()?['name']`) and error message (`item()?['error']?['message']`). Dispatch an incident alert to Teams/PagerDuty and log to Dataverse. 3. Scope_Finally: Configured to run if Scope_Catch 'is successful', 'has failed', or 'is skipped'. Houses cleanup logic (releasing machine locks, closing DB connections, updating work item status). Terminating: Always place a 'Terminate (Failed)' action at the end of Scope_Catch so the flow run is properly flagged as Failed in enterprise monitoring telemetry.
How do you architect high-throughput flows to prevent HTTP 429 'Too Many Requests' connector throttling and tenant API capacity exhaustion?
Preventing throttling requires a multi-layered defense: 1. Server-Side Filtering: Replace client-side Apply-to-Each loops with server-side OData `$filter` and `$select` queries to pull only necessary columns and rows. 2. Concurrency Control Tuning: In 'Apply to each' settings, enable Concurrency Control and dial the degree of parallelism to between 10 and 20 (max 50) based on target connector rate limits. 3. Exponential Backoff Retry Policy: In action settings, set Retry Policy to Exponential Backoff (Count: 4, Interval: PT10S, Min: PT5S, Max: PT2M) to gracefully absorb transient spikes. 4. Batching Operations: Use batch operations (e.g. Dataverse `$batch` / `executeMultiple`, SharePoint batch requests, or SQL bulk inserts) rather than individual per-row API calls. 5. Architecture Decoupling: Use Azure Service Bus or Dataverse Work Queues to decouple high-volume ingest from downstream processing.
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