DEVOPS
Agent-Driven Cardinality Investigation and Sampling Proposal
An agent investigates a flagged Honeycomb cardinality spike end to end: it runs shell queries against the schema, reasons about whether the dimension is legitimately needed.
How it runs
The automated pipeline, trigger to output.
- TriggerOperator manually starts an investigation for a dataset/dimension
- ActionRun shell-based Honeycomb queries on distinct values and usageShell
- LogicAgent judges whether to sample, drop, or keep the field
- ActionDraft the matching sampling rule or column-drop changeHoneycomb
- OutputOpen GitLab MR with the change and written rationaleGitLab
What it does
This is the judgment-heavy version of the guard. Instead of a fixed threshold, an agent investigates a flagged dimension, decides whether it is genuinely useful for queries or just noise, and proposes the right remedy: sample it, drop it, or leave it with a justification.
When to use it
Use it for the hard cases where a simple threshold over-fires, for example a `customer_id` you actually slice by versus a `trace_id` that should never be a Honeycomb dimension. The agent writes up why.
How it works
- 1A manual trigger starts an investigation against a named dataset and dimension.
- 2The agent runs shell-based Honeycomb queries to inspect distinct values, query usage of the field, and event-volume contribution.
- 3It reasons about whether the dimension earns its cardinality cost or should be sampled or dropped.
- 4It drafts the matching change: a Refinery sampling rule or a column-drop proposal.
- 5It opens a GitLab MR containing the change plus a written rationale and the supporting query evidence for human approval.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect ShellRun sandboxed commands inside the workspace.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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