AI AGENTS
Honeycomb Trace Spend PR Guardrail
When a pull request touches instrumentation code, an agent estimates the trace and span volume impact in Honeycomb and comments on the GitHub PR with the projected cost change…
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub pull request openedGitHub
- LogicDiff touches instrumentation?
- ActionQuery Honeycomb span volume and costHoneycomb
- ActionAgent projects cost impact of changeOpenAI
- OutputComment projected cost on the PRGitHub
What it does
Acts as a cost guardrail at code-review time. When a PR modifies tracing or instrumentation, an agent inspects the diff, correlates the affected spans against current Honeycomb volume and cost, and projects how the change will move span ingest. It leaves an inline GitHub PR comment so reviewers see the cost consequence before merging, not after the bill arrives.
When to use it
Use it when instrumentation changes regularly slip through review and quietly raise observability cost. Best for teams that want a shift-left signal so engineers own the spend their spans create.
How it works
- 1A GitHub pull-request event triggers the workflow.
- 2A logic step checks whether the diff touches instrumentation or tracing files; unrelated PRs exit immediately.
- 3The agent reads the affected span names and queries Honeycomb for their current volume and cost contribution.
- 4It projects the volume change implied by the diff and estimates the dollar impact.
- 5The agent posts an inline comment on the GitHub PR summarizing the projected cost change and any sampling suggestion.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect HoneycombDistributed traces and queries.
- 3Connect OpenAIModels, embeddings, files.
- 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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Run it inside a business
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