AI AGENTS

Datadog Noisy-Log Sampling Proposal to GitLab MR

Monthly, an agent clusters Datadog log patterns, identifies the highest-volume low-severity sources.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonthly schedule starts the review
  • ActionPull 30 days of indexed log analytics from DatadogDatadogDatadog
  • LogicRank clusters, filter to low-value sources
  • LogicSplit into exclusion vs sampling buckets
  • ActionOpen GitLab MR with filters and projected reductionGitLabGitLab
  • OutputNotify platform channel in SlackSlack

What it does

This agent targets Datadog log management costs. It clusters indexed log events by source and message pattern, finds the high-volume low-severity streams driving your indexing spend, and opens a GitLab merge request adding exclusion filters and sampling rates to your logging pipeline config, with a projected reduction in indexed events per month.

When to use it

Use it when Datadog indexed-log costs are the problem and your config lives in GitLab. Best for teams that want monthly, audited tuning of log exclusion filters rather than clicking through the Datadog UI.

How it works

  1. 1A monthly schedule starts the run.
  2. 2The agent pulls the last 30 days of indexed log analytics from Datadog grouped by service and pattern.
  3. 3It ranks clusters by indexed volume and filters to low-severity, low-value sources.
  4. 4A logic step splits candidates into full-exclusion versus partial-sampling buckets based on whether any signal value remains.
  5. 5It drafts exclusion filters and sampling percentages and estimates the monthly indexed-event reduction.
  6. 6It opens a GitLab merge request editing the logging pipeline config with the rules and savings summary, then notifies the platform channel in Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DatadogMetrics, traces, log search.
  2. 2
    Connect GitLabRepos, MRs, pipelines, registry.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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