SUMMARIZATION
Agentic quarterly product narrative from the full discovery corpus
Quarterly, an agent reads the full corpus of discovery-call summaries and Linear demand counts, reasons about what customers are really telling product.
How it runs
The automated pipeline, trigger to output.
- TriggerQuarterly planning schedule fires
- ActionPull quarter's VoC theme briefs from NotionNotion
- ActionCross-reference Linear demand countsLinear
- LogicAgent reasons on durability, conflicts, segments
- ActionRank candidate bets with supporting evidenceOpenAI
- OutputDraft prioritized product narrative in NotionNotion
What it does
Produces the quarterly "what should we build and why" document straight from customer evidence. An agent ingests the quarter's discovery-call theme briefs from Notion together with demand counts on Linear feature issues, reasons across the two sources to separate noise from durable signal, and drafts a prioritized product narrative: the top customer problems, the bets that address them, the evidence behind each, and what to explicitly not do. The draft lands as a Notion doc for the team to review.
When to use it
Use this ahead of quarterly planning when you want the roadmap argument grounded in the actual call corpus rather than in HiPPO opinion. It is agent-driven because it requires judgment across messy, conflicting inputs, not a fixed pipeline.
How it works
- 1A quarterly schedule starts the agent run.
- 2The agent pulls the quarter's VoC theme briefs from Notion.
- 3It cross-references Linear feature issues and their demand counts.
- 4It reasons about durability, conflicts, and customer segments to rank candidate bets with evidence.
- 5It drafts the prioritized product narrative, including a deliberate non-goals section.
- 6The draft is written to a Notion planning doc for human review.
Set it up
What you configure once, before turning it on.
- 1Connect NotionPages, databases, comments.
- 2Connect LinearIssues, projects, cycles, triage.
- 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
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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