DEVOPS

Agent that removes stale flags and verifies no rollback risk

An agent investigates each flag at 100% for 30 days, confirms the off-variation has zero recent traffic, then authors a tested GitHub PR removing the flag only when it is safe.

CategoryDevOps
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule starts the agent
  • ActionLoad 100%-for-30-days candidates from DatadogDatadogDatadog
  • LogicCheck if the off-variation still gets any traffic
  • ActionRead flag code and author removal PR in GitHubGitHubGitHub
  • OutputEscalate risky kill-switch flags to SlackSlack

What it does

This agent-driven workflow does the judgment work a human would before deleting a flag. For each flag stuck at 100% for 30+ days it checks whether the disabled variation still receives any traffic, reads the surrounding code, and only authors a removal PR when it can confirm the off path is truly dead. Risky cases get flagged for human review instead of auto-changed.

When to use it

Use it when blanket flag removal scares you — when some 100% flags are kill switches or fallbacks that must not be ripped out. The agent adds a safety verdict to every candidate so you remove the dead ones and quarantine the load-bearing ones.

How it works

A weekly schedule starts the agent. It loads candidate flags at 100% for 30 days from Datadog, then for each one queries Datadog for any evaluations of the off-variation in the last 30 days. If the off path is dead, it reads the flag's code in GitHub, removes the guard, runs the relevant tests, and opens a PR. If the off path still gets traffic or looks like a kill switch, it routes the flag to Slack with its reasoning for a human to decide.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DatadogMetrics, traces, log search.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  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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