AI AGENTS

On-call agent: Honeycomb anomaly to gated shell remediation

When a Honeycomb trigger fires, an agent diagnoses the affected service, drafts a shell remediation, and waits for a human Slack approval before executing it.

CategoryAI Agents
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps7
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerHoneycomb trigger fires on SLO breachHoneycomb
  • ActionAgent reads traces and matches runbookHoneycomb
  • LogicDraft single shell remediation with rationale
  • ActionPost proposal to on-call Slack with Approve/RejectSlack
  • LogicGate: proceed only on human Approve
  • ActionExecute approved shell command, capture outputShell
  • OutputPost execution result back to SlackSlack

What it does

Turns a Honeycomb alert into a proposed, human-approved fix. The agent reads the failing query, picks the most likely remediation (restart a worker, flush a cache, scale a pool), and never runs anything until an on-call engineer clicks Approve.

When to use it

Use it when you have a Honeycomb SLO or trigger watching a service and want faster mean-time-to-recovery without giving an agent unsupervised shell access to production.

How it works

  1. 1A Honeycomb trigger posts the breaching query, dataset, and result to the workflow.
  2. 2The agent pulls recent traces and the matching runbook entry to identify the root cause.
  3. 3It composes a single concrete shell command plus a plain-English rationale and expected effect.
  4. 4The proposal is sent to the on-call Slack channel with Approve and Reject buttons.
  5. 5On Approve, the shell action runs the exact command and captures stdout, stderr, and exit code.
  6. 6The agent posts the result back to Slack and closes the loop, or escalates if the command fails.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HoneycombDistributed traces and queries.
  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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