AI AGENTS
Gate sprint-candidate stories on grooming readiness
Before each sprint starts, an agent audits every candidate issue for clear criteria and a sane estimate, splits or re-estimates the failures.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule fires on planning morning
- ActionPull next-sprint candidate issuesLinear
- ActionAudit criteria clarity and estimate fitOpenAI
- LogicClassify ready / oversized / under-estimated
- ActionSplit or re-estimate failures in LinearLinear
- OutputPost go/no-go readiness report to channelSlack
What it does
This agent runs a readiness gate over your sprint candidates. For each issue tagged for the upcoming sprint, it checks two things: are the acceptance criteria specific and testable, and is the estimate consistent with the scope those criteria describe. Issues that fail get auto-split or re-estimated; issues that pass are marked sprint-ready. The result is a single go/no-go report.
When to use it
Use it the morning of sprint planning so the team enters the meeting knowing exactly which candidates are ready and which need decisions. It turns a long manual review into a five-minute scan of flagged exceptions.
How it works
- 1A schedule fires the morning planning is held.
- 2The agent pulls all issues tagged for the next sprint from Linear.
- 3An LLM audits each for criteria clarity and estimate-to-scope fit, classifying ready, oversized, or under-estimated.
- 4A logic step routes each class to the right remediation.
- 5The agent splits oversized issues and re-estimates under-pointed ones in Linear, marking the rest sprint-ready.
- 6It posts a go/no-go readiness report to the team channel listing every action taken and every issue still needing a human decision.
Set it up
What you configure once, before turning it on.
- 1Connect LinearIssues, projects, cycles, triage.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 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.
More AI Agents workflows
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Resolved Incident to Public Troubleshooting Doc
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On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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