AI AGENTS

Nightly Saturation Sweep with Approval-Gated Scaling

On a nightly schedule, an agent reviews Datadog capacity trends, predicts resources nearing saturation.

CategoryAI Agents
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires
  • ActionQuery Datadog capacity trendsDatadogDatadog
  • LogicFlag resources nearing saturation
  • ActionAgent drafts scaling/cleanup proposals
  • OutputPost digest to Slack with Approve controlsSlack
  • ActionOn approval, execute via shellShell

What it does

Proactively heads off capacity incidents instead of waiting to be paged. On a schedule, the agent reads Datadog trends for disk, memory, connection pools, and queue depth, projects which resources will hit their limits soon, and drafts preemptive remediation. The operator approves before any scaling or cleanup runs.

When to use it

Use it when slow-burn saturation issues (disks filling, pools exhausting) repeatedly turn into 3 a.m. pages. Running it nightly converts those into a calm, batched morning decision with a clear proposed action.

How it works

  1. 1A nightly schedule triggers the flow.
  2. 2The agent queries Datadog for capacity and utilization trends across watched resources.
  3. 3A logic step flags resources whose projected trajectory crosses a saturation threshold.
  4. 4For each flagged resource the agent drafts a specific scaling or cleanup action with the command and expected headroom gained.
  5. 5The proposals are posted to Slack as a digest with per-item Approve controls.
  6. 6On approval, the shell action executes the chosen items and reports the new utilization.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DatadogMetrics, traces, log search.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  3. 3
    Connect ShellRun sandboxed commands inside the workspace.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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