get_agent_goal ¶

def get_agent_goal(
    self,
    profile_arn: "capo_wellarchitected.types.agent_profile_arn.AgentProfileArn",
    id: "capo_wellarchitected.types.uuid.UUID",
    *,
    config_overrides: Optional[WellArchitectedClientConfig] = None,
) -> "capo_wellarchitected.types.get_agent_goal_response.GetAgentGoalResponse":
    """<p>Retrieves detailed information about a specific optimization goal.</p>

    Args:
        profile_arn: <p>The Amazon Resource Name (ARN) of the profile containing the goal.</p>
        id: <p>The unique identifier of the goal to retrieve.</p>

    Raises:
        capo_wellarchitected.errors.access_denied_exception.AccessDeniedException: <p>User does not have sufficient access to perform this action.</p>
        capo_wellarchitected.errors.internal_server_exception.InternalServerException: <p>There is a problem with the Well-Architected Tool API service.</p>
        capo_wellarchitected.errors.resource_not_found_exception.ResourceNotFoundException: <p>The requested resource was not found.</p>
        capo_wellarchitected.errors.throttling_exception.ThrottlingException: <p>Request was denied due to request throttling.</p>
        capo_wellarchitected.errors.validation_exception.ValidationException: <p>The user input is not valid.</p>
        capo_wellarchitected.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_wellarchitected.types.get_agent_goal_request.GetAgentGoalRequest]",
    ) -> OperationResponse[
        "capo_wellarchitected.types.get_agent_goal_response.GetAgentGoalResponse"
    ]:
        import capo_wellarchitected._operations.well_architected_api_service_lambda.get_agent_goal

        output, http_response = (
            capo_wellarchitected._operations.well_architected_api_service_lambda.get_agent_goal.get_agent_goal(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_output(
        output: "capo_wellarchitected.types.get_agent_goal_response.GetAgentGoalResponse",
    ) -> dict[str, Any]:
        import capo_wellarchitected.types.get_agent_goal_response

        return capo_wellarchitected.types.get_agent_goal_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("GetAgentGoal", redact_output=_redact_output),
    )
    input_: capo_wellarchitected.types.get_agent_goal_request.GetAgentGoalRequest = {
        "profile_arn": profile_arn,
        "id": id,
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output