1. Datadog-to-Slack AI Workflow Metrics and Alerts Communication


    To set up a Datadog-to-Slack workflow for metrics and alerts communication, you will typically need to do the following:

    1. Set up a Datadog monitor that tracks the specific metrics you are interested in alerting on.
    2. Configure a Datadog notification to send alerts to a Slack channel when the monitor's conditions are met.

    However, in the context of Pulumi, what we can do is define the resources for these configurations as code. To automate the process, you need to use the Datadog provider to create the necessary Datadog resources and configure the notifications to be sent to Slack. Note that you would need to have the Datadog API and application keys available and that you have already set up your Slack workspace with a channel intended for receiving these alerts; those setup steps are outside the scope of Pulumi's responsibility.

    Here's a Pulumi program in Python that automates the creation of a Datadog metric monitor which sends notifications to a configured Slack channel:

    import pulumi import pulumi_datadog as datadog # Define a new Datadog monitor for tracking a specific metric. # Replace `your_metric` with the actual metric you're interested in monitoring. metric_monitor = datadog.Monitor("ai_workflow_monitor", type="query alert", query="avg(last_5m):avg:your_metric{environment:production} > 95", name="AI Workflow High Metric Alert", message="Notification @channel_name, the AI Workflow metric is too high!", tags=["ai-workflow", "metrics"], # The following options can be modified as per your alerting requirements. options=datadog.MonitorOptionsArgs( thresholds=datadog.MonitorThresholdsArgs( critical=95.0, critical_recovery=90.0, ), notify_audit=False, locked=False, timeout_h=0, new_host_delay=300, require_full_window=True, notify_no_data=False, renotify_interval=0, escalation_message="Escalation message @pagerduty", no_data_timeframe=20, include_tags=True, evaluation_delay=15, ) ) # Output the URL of the monitor dashboard pulumi.export('monitor_url', pulumi.Output.concat('https://app.datadoghq.com/monitors/', metric_monitor.id))

    In the above program, we are creating a monitor resource using the Datadog provider. We define a query that specifies the metric we are interested in and the conditions that trigger an alert. The type of the monitor is set to "query alert" which means it will evaluate a query over a time period and trigger an alert if the conditions are met.

    The message property contains the text of the alert message that will be sent out. The @channel_name should be replaced with your actual Slack channel name. Moreover, we configure the monitor with tags for easier filtering and management, as well as other options like alert thresholds, new host delay, and evaluation delay.

    The message also includes an @pagerduty service, which would be used to integrate with PagerDuty if you're using it alongside Slack for incident management. You should replace this with your actual PagerDuty service name or remove it if not used.

    Furthermore, the program exports the monitor URL so that you can easily access it from the Datadog dashboard.

    Please note that you will need to handle the Slack integration setup within the Datadog platform manually, as this requires interaction with the Slack API and is generally not managed through infrastructure code. Once the Slack integration is set up in Datadog, the alert messages will be automatically posted to the specified Slack channel when the monitor conditions are met.

    You will also need to install the Datadog Pulumi provider and configure it with your Datadog API and application keys. This is typically done through setting up the appropriate environment variables DATADOG_API_KEY and DATADOG_APP_KEY before running pulumi up to deploy the resources.