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Inspect PAC CLI export/import steps, environment permissions, build logs, package version, preflight checks and postdeployment smoke tests. These checks are relevant to Power Platform pipelines and CI deployment gates, but are not the most discriminating first evidence for this case. Inspect instead: Review active layer, publisher, managed property, patch versus upgrade, dependency and current component state.

Compare data throughput, TTL, partition strategy, supported relationship behavior and required plug-in or query features. These checks are relevant to Elastic table and high-volume data pattern, but are not the most discriminating first evidence for this case. Inspect instead: Review active layer, publisher, managed property, patch versus upgrade, dependency and current component state.

Inspect environment-variable definition/default/current values, connection-reference binding, deployment settings and secret-storage policy. These checks are relevant to Environment variables and connection references, but are not the most discriminating first evidence for this case. Inspect instead: Review active layer, publisher, managed property, patch versus upgrade, dependency and current component state.

Inspect evaluator cases, tool traces, policy refusal tests, human approval status, error rates and rollback evidence. These checks are relevant to Agent evaluation and human approval for side effects, but are not the most discriminating first evidence for this case. Inspect instead: Inspect execution depth, parent context, plugin registration stages, update attributes and processing marker.

Inspect output parameters, stage, post image configuration, async mode, transaction state and external dependencies. These checks are relevant to Dataverse PostOperation plug-in and after-save evidence, but are not the most discriminating first evidence for this case. Inspect instead: Inspect execution depth, parent context, plugin registration stages, update attributes and processing marker.

Inspect assembly version, registered type, message filters, sandbox mode, image alias and effective solution layer. These checks are relevant to Plug-in Registration Tool and signed assembly deployment, but are not the most discriminating first evidence for this case. Inspect instead: Inspect execution depth, parent context, plugin registration stages, update attributes and processing marker.

The additional value is saved atomically with the original request without an extra update recursion. This result validates Dataverse PreOperation Target changes instead of the component in question. Here the specific acceptance check is: An agent answer is attributable to current permitted records and does not expose inaccessible employee data.

The unattended flow continues after an employee leaves and fails safely on permission or data errors. This result validates Dataverse connector service principal and retry policies instead of the component in question. Here the specific acceptance check is: An agent answer is attributable to current permitted records and does not expose inaccessible employee data.

Long-running processing completes independently of the initiating save and reports its final status later. This result validates Azure Functions for long-running Dataverse work instead of the component in question. Here the specific acceptance check is: An agent answer is attributable to current permitted records and does not expose inaccessible employee data.

Inspect ordering needs, delivery guarantees, subscriber model, retry/dead letter, throughput and data contract. These checks are relevant to Azure Service Bus and Event Hub endpoint selection, but are not the most discriminating first evidence for this case. Inspect instead: Inspect authorization URL, scopes, redirect URI, token audience, policy template rules and deployment connection references.

Inspect message pipeline stage, endpoint configuration, event payload, caller identity, service bus connectivity and logs. These checks are relevant to Dataverse IServiceEndpointNotificationService event publish, but are not the most discriminating first evidence for this case. Inspect instead: Inspect authorization URL, scopes, redirect URI, token audience, policy template rules and deployment connection references.

Inspect Swagger/OpenAPI version, base URL, parameter types, request/response schema and missing operation IDs. These checks are relevant to Custom connector OpenAPI and operation identifiers, but are not the most discriminating first evidence for this case. Inspect instead: Inspect authorization URL, scopes, redirect URI, token audience, policy template rules and deployment connection references.

Invoke a solution-managed cloud flow from a code app with well-defined input/output schemas and bounded timeout/error handling. That action addresses Code app use of Power Automate cloud flow instead. Calling a flow asynchronously does not guarantee its downstream actions committed before a UI success toast. For this case, the targeted action is: Use Application Insights telemetry with correlation IDs and privacy-safe fields to monitor code-app failures and end-to-end calls.

Build dataset PCF components that use provided paging, sorting and selection APIs rather than assuming all rows are loaded. That action addresses PCF dataset paging and selection behavior instead. Data-set PCF hosts may supply paged data; a single loaded page is not a complete table. For this case, the targeted action is: Use Application Insights telemetry with correlation IDs and privacy-safe fields to monitor code-app failures and end-to-end calls.

Read environment variables via supported Power Platform mechanism and keep endpoints, feature flags and tenant configuration separate from code. That action addresses Code app environment variables and release-specific config instead. Client-exposed configuration must not contain secrets; environment variables do not make embedded keys confidential. For this case, the targeted action is: Use Application Insights telemetry with correlation IDs and privacy-safe fields to monitor code-app failures and end-to-end calls.

Build dataset PCF components that use provided paging, sorting and selection APIs rather than assuming all rows are loaded. That action addresses PCF dataset paging and selection behavior instead. Data-set PCF hosts may supply paged data; a single loaded page is not a complete table. For this case, the targeted action is: Avoid redundant network calls, unnecessary re-rendering and leaked listeners; use pagination, memoization and cleanup when appropriate.

Use supported code-app Dataverse data APIs to perform CRUD and read table metadata with correct logical names, permissions and paging. That action addresses Code app Dataverse metadata and table operations instead. Display labels can differ from Dataverse logical names and a successful connection does not imply every table privilege. For this case, the targeted action is: Avoid redundant network calls, unnecessary re-rendering and leaked listeners; use pagination, memoization and cleanup when appropriate.

Use Power Apps Monitor and browser developer tools to reproduce request errors, isolate code failures and distinguish host from API issues. That action addresses Code app Monitor and browser troubleshooting instead. A failed HTTP call may be denied by authorization or an invalid query rather than an application crash. For this case, the targeted action is: Avoid redundant network calls, unnecessary re-rendering and leaked listeners; use pagination, memoization and cleanup when appropriate.

The rule works for supported users without maintaining custom code and the exception cases remain correct. This result validates Out-of-box table/form choice versus server extension instead of the component in question. Here the specific acceptance check is: The same solution promotes through dev and prod without embedded environment URLs or personal-user connections.

A blocked quality gate prevents production import while an approved release is traceable to a specific package hash. This result validates Power Platform pipelines and CI deployment gates instead of the component in question. Here the specific acceptance check is: The same solution promotes through dev and prod without embedded environment URLs or personal-user connections.

Operations staff can trace a failing user request across components without exposing customer personal data. This result validates Cross-service monitoring and alert design instead of the component in question. Here the specific acceptance check is: The same solution promotes through dev and prod without embedded environment URLs or personal-user connections.

Inspect message name, stage, execution context IsInTransaction, parent request, caller privileges and validation dependencies. These checks are relevant to Dataverse PreValidation plug-in and aborting operations, but are not the most discriminating first evidence for this case. Inspect instead: Inspect trigger type, correlation ID, Dataverse service principal, function timeout, queue visibility and failed-message policy.

Inspect token audience/scopes, app registration, app-user Dataverse role, expiration and token cache security. These checks are relevant to OAuth delegated vs client credential in Dataverse, but are not the most discriminating first evidence for this case. Inspect instead: Inspect trigger type, correlation ID, Dataverse service principal, function timeout, queue visibility and failed-message policy.

Inspect identity object ID, token audience, Dataverse app user, table privileges, role assignments and secret usage. These checks are relevant to Managed identity Azure Function to Dataverse, but are not the most discriminating first evidence for this case. Inspect instead: Inspect trigger type, correlation ID, Dataverse service principal, function timeout, queue visibility and failed-message policy.

Review manifest input/output types, updateView context, change notification frequency, cleanup and rendered DOM subscriptions. These checks are relevant to PCF lifecycle methods and output notification, but are not the most discriminating first evidence for this case. Inspect instead: Inspect solution variable current values, environment ownership, deployment settings and fallback behavior.

Inspect command designer placement, selection context, Power Fx formula, app publication and calling user's security role. These checks are relevant to Command bar Power Fx and JavaScript rules, but are not the most discriminating first evidence for this case. Inspect instead: Inspect solution variable current values, environment ownership, deployment settings and fallback behavior.

Compare available business-rule scopes, related-row access, API restrictions, field change triggers and testability. These checks are relevant to Form business rule versus custom client JavaScript, but are not the most discriminating first evidence for this case. Inspect instead: Inspect solution variable current values, environment ownership, deployment settings and fallback behavior.

Use a supported Copilot Studio client library to invoke agents from app code while handling sessions, authorization, response events and cancellation. This step belongs to Copilot Studio client library consumption rather than the complementary pair here. The required action is: Expose Power Platform operations to Foundry agents using governed MCP servers and explicit tool schemas, permissions and audit traces. And its evidence is: Inspect MCP tool descriptions, allowed identity, read/write separation, approval gates, payload validation and agent run logs.

Inspect execution depth, parent context, plugin registration stages, update attributes and processing marker. This step belongs to Plug-in recursion depth and idempotent registration rather than the complementary pair here. The required action is: Expose Power Platform operations to Foundry agents using governed MCP servers and explicit tool schemas, permissions and audit traces. And its evidence is: Inspect MCP tool descriptions, allowed identity, read/write separation, approval gates, payload validation and agent run logs.

The unattended flow continues after an employee leaves and fails safely on permission or data errors. This step belongs to Dataverse connector service principal and retry policies rather than the complementary pair here. The required action is: Expose Power Platform operations to Foundry agents using governed MCP servers and explicit tool schemas, permissions and audit traces. And its evidence is: Inspect MCP tool descriptions, allowed identity, read/write separation, approval gates, payload validation and agent run logs.

Package PCF through supported tooling, include built assets in a solution and bind the component to the intended column or dataset. That action addresses Packaging and deploying PCF in solution instead. A successful npm build is not equivalent to deploying and configuring a PCF in the target environment. For this case, the targeted action is: Use Power Apps Monitor and browser developer tools to reproduce request errors, isolate code failures and distinguish host from API issues.

Configure a code app's Power Platform connections and permissions using supported connection references rather than direct embedded API secrets. That action addresses Power Apps code app connectors and environment binding instead. Code apps still require appropriate app and data-source authorization even with a custom React frontend. For this case, the targeted action is: Use Power Apps Monitor and browser developer tools to reproduce request errors, isolate code failures and distinguish host from API issues.

Deploy a versioned code app through the supported Power Platform workflow and share it with explicit app and data permissions. That action addresses Code app packaging sharing and deployment instead. Sharing an app does not automatically grant Dataverse table rights or connector access. For this case, the targeted action is: Use Power Apps Monitor and browser developer tools to reproduce request errors, isolate code failures and distinguish host from API issues.