DEVOPS
Agent-Driven Honeycomb SLO Forecast and Capacity Brief
An agent reviews Honeycomb burn-rate trends across services, writes a reasoned capacity brief explaining which SLOs need attention and why.
How it runs
The automated pipeline, trigger to output.
- TriggerMonthly schedule fires
- ActionFetch SLO burn and budget history from HoneycombHoneycomb
- LogicAgent classifies and prioritizes each SLO
- ActionPublish capacity brief to ConfluenceConfluence
- OutputCreate per-recommendation follow-up on Mondaymonday.com
What it does
A Paperclip agent pulls burn-rate and budget data for each Honeycomb SLO, reasons over the trends to decide which services genuinely need a capacity review (versus noisy short-term spikes), and writes a plain-language capacity brief. It publishes the brief to Confluence and opens a Monday follow-up item for each recommended review. The outcome is a judgment-backed planning document rather than a raw metric dump.
When to use it
Use this when you want analysis and prioritization, not just thresholds — for example a monthly capacity planning input where context and trade-offs matter and a human reads the brief before committing sprint capacity.
How it works
- 1A monthly schedule trigger starts the run.
- 2The agent fetches per-SLO burn rate, budget history, and recent burn events from Honeycomb.
- 3It reasons over the data to classify each SLO as healthy, watch, or needs-review, with a written rationale.
- 4It publishes the consolidated capacity brief as a Confluence page.
- 5For each needs-review SLO it creates a Monday follow-up with the agent's recommendation attached.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Connect monday.comVisual work management for teams.
- 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 this workflow in your colony.
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