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The agent executes only permitted actions and produces traceable responses under the invoking user's entitlements. This result validates Agent integration architecture using Copilot Studio instead of the component in question. Here the specific acceptance check is: The application reads the authoritative source while all required edits and security operations behave as documented.

The service accesses only approved rows and forbidden updates fail even when made through a direct Web API call. This result validates Least-privilege technical authorization design instead of the component in question. Here the specific acceptance check is: The application reads the authoritative source while all required edits and security operations behave as documented.

The UX extension runs in its intended host, remains maintainable and respects platform permissions. This result validates Choosing client scripts PCF or code apps instead of the component in question. Here the specific acceptance check is: The application reads the authoritative source while all required edits and security operations behave as documented.

An agent answer is attributable to current permitted records and does not expose inaccessible employee data. This result validates Work IQ grounded Dataverse access for agents instead of the component in question. Here the specific acceptance check is: Unexpected instructions are refused and sensitive updates occur only with recorded authorization and verification.

The agent reads authorized records and cannot modify a customer account through an undeclared or unapproved tool. This result validates Microsoft Foundry agent tool access via MCP servers instead of the component in question. Here the specific acceptance check is: Unexpected instructions are refused and sensitive updates occur only with recorded authorization and verification.

Related writes either commit in the intended atomic scope or fail with precise recoverable evidence. This result validates ExecuteMultiple transactions and batch scope instead of the component in question. Here the specific acceptance check is: Unexpected instructions are refused and sensitive updates occur only with recorded authorization and verification.

The component degrades predictably on an unsupported host without exposing data or crashing. This result validates PCF WebAPI Device and Utility usage instead of the component in question. Here the specific acceptance check is: The dataset honors the host's filters and loads additional pages without double-counting or stale selections.

Only entitled users see and can execute the command, while the operation is still protected by server authorization. This result validates Command bar Power Fx and JavaScript rules instead of the component in question. Here the specific acceptance check is: The dataset honors the host's filters and loads additional pages without double-counting or stale selections.

The code app loads necessary resources under restrictive policy and blocks unauthorized third-party script execution. This result validates Code app Content Security Policy configuration instead of the component in question. Here the specific acceptance check is: The dataset honors the host's filters and loads additional pages without double-counting or stale selections.

Use configured Dataverse table relationships and a business rule before adding a custom plug-in when requirements fit declarative capabilities. This step belongs to Out-of-box table/form choice versus server extension rather than the complementary pair here. The required action is: Choose a standard Dataverse table for native persistence and offline-style features, and a virtual table when live external rows must remain at their source and provider limitations are accepted. And its evidence is: Check data ownership, virtual table provider, supported CRUD behavior, security, auditing and expected query performance.

Inspect data location, required transaction participation, authentication type, operation contract, caller scope and rate limits. This step belongs to Custom connector boundary versus plug-in endpoint rather than the complementary pair here. The required action is: Choose a standard Dataverse table for native persistence and offline-style features, and a virtual table when live external rows must remain at their source and provider limitations are accepted. And its evidence is: Check data ownership, virtual table provider, supported CRUD behavior, security, auditing and expected query performance.

The custom agent and Power Platform app exchange only explicitly authorized structured requests and log outcomes. This step belongs to Microsoft Foundry versus Power Platform agent boundary rather than the complementary pair here. The required action is: Choose a standard Dataverse table for native persistence and offline-style features, and a virtual table when live external rows must remain at their source and provider limitations are accepted. And its evidence is: Check data ownership, virtual table provider, supported CRUD behavior, security, auditing and expected query performance.

Inspect source authoritative field, version/ETag, deleted marker, reprocessing window and recovery checkpoint. These checks are relevant to Incremental sync conflict and deletion recovery, but are not the most discriminating first evidence for this case. Inspect instead: Inspect Swagger/OpenAPI version, base URL, parameter types, request/response schema and missing operation IDs.

Inspect Function authentication, request/response mapping, OpenAPI action, error status, timeout and log correlation. These checks are relevant to Azure Function-backed connector transformation, but are not the most discriminating first evidence for this case. Inspect instead: Inspect Swagger/OpenAPI version, base URL, parameter types, request/response schema and missing operation IDs.

Inspect table tracking setting, change tracking token age, paging, deletion metadata and downstream reconciliation. These checks are relevant to Dataverse change tracking incremental synchronization, but are not the most discriminating first evidence for this case. Inspect instead: Inspect Swagger/OpenAPI version, base URL, parameter types, request/response schema and missing operation IDs.

The same solution promotes through dev and prod without embedded environment URLs or personal-user connections. This result validates Environment variables and connection references instead of the component in question. Here the specific acceptance check is: A deployment changes only the intended solution layer and the previous customization is recoverable.

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: A deployment changes only the intended solution layer and the previous customization is recoverable.

Callers reach the intended business operation through a stable contract with its security boundary explicit. This result validates Custom connector boundary versus plug-in endpoint instead of the component in question. Here the specific acceptance check is: A deployment changes only the intended solution layer and the previous customization is recoverable.

The custom agent and Power Platform app exchange only explicitly authorized structured requests and log outcomes. This result validates Microsoft Foundry versus Power Platform agent boundary 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.

Keep related Dataverse tables, flows, connection references and plug-in registrations in a versioned solution and resolve dependencies before import. That action addresses Solution component dependency management instead. A flow in a solution still requires its connector references and dependent tables to be present in the target. For this case, the targeted action is: Use solution environment variables for per-environment configuration and connection references for connector bindings, rather than hardcoded identifiers or secrets.

Evaluate an elastic table for high-volume partitioned Dataverse workloads only after confirming partition keys, feature limits and transactional requirements. That action addresses Elastic table and high-volume data pattern instead. Elastic tables differ from standard tables; their scale benefits do not eliminate data-model and partition tradeoffs. For this case, the targeted action is: Use solution environment variables for per-environment configuration and connection references for connector bindings, rather than hardcoded identifiers or secrets.

Choose a standard Dataverse table for native persistence and offline-style features, and a virtual table when live external rows must remain at their source and provider limitations are accepted. That action addresses Standard versus virtual Dataverse tables instead. Virtual tables surface external data but do not automatically inherit every capability of Dataverse-stored tables. For this case, the targeted action is: Use solution environment variables for per-environment configuration and connection references for connector bindings, rather than hardcoded identifiers or secrets.

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, filtering attributes, change type, OData row filter, trigger condition and run history.

Inspect action statuses, timeout values, scope execution tree, compensating action and per-run correlation. These checks are relevant to Power Automate Scope run-after and failure handling, but are not the most discriminating first evidence for this case. Inspect instead: Inspect trigger type, filtering attributes, change type, OData row filter, trigger condition and run history.

Inspect environment and agent IDs, user auth, session lifecycle, tool event handling, response streaming and retries. These checks are relevant to Copilot Studio client library consumption, but are not the most discriminating first evidence for this case. Inspect instead: Inspect trigger type, filtering attributes, change type, OData row filter, trigger condition and run history.

Multiple callers invoke one stable server-side command that validates inputs and applies consistent authorization. This result validates Dataverse custom API contract and plug-in execution instead of the component in question. Here the specific acceptance check is: A function retrieves only authorized Dataverse rows without shipping plaintext client secrets in configuration.

Secrets stay out of ordinary run logs while operations retain auditable success and failure metadata. This result validates Secure inputs outputs Key Vault and secret handling instead of the component in question. Here the specific acceptance check is: A function retrieves only authorized Dataverse rows without shipping plaintext client secrets in configuration.

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: A function retrieves only authorized Dataverse rows without shipping plaintext client secrets in configuration.

Use durable checkpoints, conflict detection and explicit deletion reconciliation to keep external systems consistent with Dataverse. This step belongs to Incremental sync conflict and deletion recovery rather than the complementary pair here. The required action is: Configure OAuth or supported API key authentication and policy templates for runtime headers/URLs without placing secrets in app formulas. And its evidence is: Inspect authorization URL, scopes, redirect URI, token audience, policy template rules and deployment connection references.

Inspect delivery attempt logs, event ID, consumer receipt store, downstream transaction and retry/backoff handling. This step belongs to Dataverse event endpoint deduplication and retry rather than the complementary pair here. The required action is: Configure OAuth or supported API key authentication and policy templates for runtime headers/URLs without placing secrets in app formulas. And its evidence is: Inspect authorization URL, scopes, redirect URI, token audience, policy template rules and deployment connection references.

An incremental sync catches updates and deletions without rescanning every row or missing changes during paging. This step belongs to Dataverse change tracking incremental synchronization rather than the complementary pair here. The required action is: Configure OAuth or supported API key authentication and policy templates for runtime headers/URLs without placing secrets in app formulas. And its evidence is: Inspect authorization URL, scopes, redirect URI, token audience, policy template rules and deployment connection references.