AI AGENTS
Buy-vs-build-vs-partner question scored into a Linear decision issue
Given a capability question, the agent researches options across the three paths, scores them on cost, speed, and risk, and opens a Linear issue with a ranked recommendation…
How it runs
The automated pipeline, trigger to output.
- TriggerOperator runs the capability question manually
- LogicDecompose into buy / build / partner sub-queries
- ActionResearch options and pricing per pathExa
- ActionScore paths on cost, speed, control, riskOpenAI
- LogicRank paths; require a source per score driver
- OutputOpen Linear issue with ranked recommendationLinear
What it does
Answers "should we buy it, build it, or partner for it?" with structure. The agent researches each path for a given capability, scores them against weighted criteria, and files a Linear issue holding a ranked recommendation, the scoring rationale, and source links so the team can act on it.
When to use it
When evaluating a new capability, vendor category, or feature where the real question is the sourcing strategy, not just feasibility. Turns a hallway debate into a tracked, evidence-backed decision.
How it works
- 1An operator triggers the run manually with the capability question.
- 2The agent decomposes into the three paths and the sub-queries each path needs (vendors, build effort, partner candidates).
- 3It runs web research per path to gather options and current pricing or precedent.
- 4An LLM step scores each path on cost, speed, control, and risk against a weighted rubric.
- 5A logic step ranks the paths and requires a citation behind every score driver.
- 6A Linear issue is opened with the ranked recommendation, scorecard, and sources, ready for triage.
Set it up
What you configure once, before turning it on.
- 1Connect ExaNeural search across the web.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 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
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
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.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.