Power Automate: Auto-Cancel Approvals

Key insights

• Overview: Mike Givens demonstrated a practical pattern for cleaning up pending approvals using Power Automate with Dataverse on the Microsoft 365 & Power Platform community call (18 December).
  The demo focuses on preventing approval requests from remaining active after flows end.
• Problem: Approval requests can become orphaned approvals, leaving approvers with stale items and leaving flows in a waiting state.
  This causes user confusion and can block long-running automation.
• Core technique: Locate the approval record in the Approvals table (use List rows with a filter to find the Approval ID) and then apply Update a row in Dataverse to change the approval status.
  This direct update lets you cancel approvals programmatically when requests are withdrawn or time out.
• Key fields to set: Update these fields to reflect a cancellation: set Stage = Complete, Status Reason = Canceled, Result = Canceled, and Status = Inactive.
  Note: some alternate values like "Expired" may not behave reliably, so use the recommended values above.
• Triggers and patterns: Use time-based triggers (for example a timeout or scheduled check), reminders, or parallel branches to detect stale approvals and run the cancellation logic.
  Storing approvals in Dataverse also supports long-running flows and governance for requests that last days or weeks.
• Benefits and caveats: This approach prevents flow stalls, creates an immediate audit trail in the Approvals center, and automates cleanup for edge cases.
  It requires Dataverse access and a premium license, so test flows and verify results in the Approvals center and flow history before wide rollout.

Overview of the demo video

The Microsoft-produced video, presented by Mike Givens on the Microsoft 365 & Power Platform Community call, demonstrates a practical pattern for handling stuck approvals in automated flows. In the recording, the presenter focuses on how to detect and cancel orphaned approval requests so that users do not see outdated items in Teams or Outlook. Consequently, the session emphasizes real-world governance and flow reliability for long-running approval processes. The video serves teams that rely on automated approvals and need predictable cleanup behavior.

How the pattern works in practice

At its core, the approach relies on using Power Automate flows together with Dataverse records to change approval state programmatically. First, flows locate the approval row in the Approvals table, then update fields such as state, status reason, and result to mark the approval as canceled or inactive. This direct record update signals to downstream waiting actions that the approval is no longer pending, and it prevents flows from remaining in indefinite wait states. As a result, teams can implement timeouts, reminders, and automatic cleanup while keeping the user experience consistent.

Step-by-step mechanics and triggers

The demo shows common triggers such as explicit timeouts, parallel branches, and checks for withdrawn requests that start the cancellation logic. Next, the flow uses List rows to find the approval by title or identifier and then applies an Update a row action on the approval record to change its lifecycle fields. After the update, the change appears in the Approvals center and in user clients, so approvers see the correct status without manual intervention. Therefore, the pattern decouples the sending of approvals from the response handling, which is useful for flows that might run for days or weeks.

Benefits and tradeoffs to consider

This Dataverse-based approach brings clear benefits: it reduces user confusion, creates an auditable trail, and supports scalable handling of long-running approvals. Moreover, because changes are visible in the Approvals center, teams gain transparency into who canceled requests and when they were finalized. However, the pattern has tradeoffs: it requires Dataverse access and typically a premium license, which may add cost and administrative overhead for some tenants. Therefore, organizations must balance improved reliability and visibility against licensing and platform access constraints.

Challenges and governance implications

Implementing automatic approval cancellation introduces governance questions, particularly around who may update approval records and how to preserve audit integrity. For instance, altering status fields directly can produce accurate user-facing results but may bypass built-in approval lifecycle controls if not properly logged and secured. Additionally, government tenants or restricted environments that lack Dataverse must rely on manual changes or custom APIs, which complicates standardization. Consequently, teams should define clear policies, roles, and logging so automated changes remain transparent and compliant.

Technical limitations and error handling

Another challenge is ensuring the update logic handles edge cases such as suspended approvals, network failures, or concurrent updates from multiple flows. The demo highlights the need for careful filtering when retrieving approval rows to avoid updating the wrong record, and for retries or compensating actions if updates fail. Furthermore, Microsoft platform changes have sometimes affected direct row updates in the past, so maintaining the pattern requires ongoing validation after platform updates. Thus, teams should plan for monitoring, error alerts, and periodic review of flow behavior.

Operational best practices

To reduce risk and improve maintainability, the video recommends testing the pattern end to end and adding clear naming conventions for approval records so flows can match them reliably. Also, use parallel branches or scheduled checks to enforce timeouts and send reminder messages before cancellation, which balances user convenience with process hygiene. In addition, keep audit fields and logging intact so administrators can trace who or what canceled an approval and why. By following these steps, organizations can gain reliable automation without sacrificing control.

Testing, monitoring, and verification

Testing should include scenarios for normal approvals, withdrawn requests, long timeouts, and failure modes to validate that the user experience matches expectations across clients like Teams and Outlook. Moreover, teams should monitor flow run histories and Dataverse logs to detect failed cancellations or unintended changes quickly. Regular verification after platform updates is also important because connector behaviors can shift and affect automation. Therefore, active monitoring and automated tests help preserve reliability over time.

When to choose this pattern

This approach makes the most sense for organizations that need long-running or mission-critical approvals and can provision Dataverse access and the associated licensing. If a team cannot use Dataverse, then alternatives such as manual cleanup, custom storage, or API-driven approaches may work but require more development and governance work. Hence, decision-makers should weigh the cost of premium features against the value of automated cleanup, auditability, and reduced user friction.

Conclusion and editorial takeaway

In summary, the Microsoft video provides a clear and practical pattern for automating approval cancellations by updating Dataverse records from Power Automate flows. It demonstrates how to reduce orphaned approvals, improve user clarity, and maintain auditable records, while also highlighting licensing and governance tradeoffs. Ultimately, teams adopting this pattern should prepare for testing, monitoring, and policy design to keep automation predictable and secure. The demo offers a useful blueprint for organizations seeking more reliable approval workflows.

Keywords

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