AI AGENTS
Quarterly Multi-Competitor Landscape Teardown
Once a quarter, the agent runs deep teardowns on every competitor in a Notion list, cross-critiques them for consistency, synthesizes a comparative landscape report.
How it runs
The automated pipeline, trigger to output.
- TriggerQuarterly schedule fires
- ActionRead competitor list from Notion databaseNotion
- ActionPer competitor: gather sources and draft teardownPerplexity
- LogicCross-critique set for contradictions and uneven depth
- ActionReconcile and synthesize comparative landscapeOpenAI
- OutputSave final report to shared Google Drive folderGoogle Drive
What it does
It produces your quarterly competitive landscape in one pass. The agent tears down each competitor individually, then critiques the whole set for inconsistent or contradictory claims before writing a single comparative report.
When to use it
When leadership expects a quarterly competitive update and assembling it by hand eats a week of analyst time. Use it to generate a consistent, cross-checked draft the team can polish.
How it works
- 1A quarterly schedule triggers the run and reads the competitor list from a Notion database.
- 2For each competitor, Perplexity gathers current sources and the agent drafts an individual teardown.
- 3A cross-critique pass compares all teardowns, flagging contradictions, uneven depth, and claims that don't hold up across the set.
- 4The agent reconciles the flagged issues and synthesizes a comparative landscape — positioning map, threat ranking, and shifts since last quarter.
- 5It assembles the final formatted document.
- 6The completed report is saved to the shared Google Drive competitive-intel folder for distribution.
Set it up
What you configure once, before turning it on.
- 1Connect NotionPages, databases, comments.
- 2Connect PerplexitySearch-grounded answers with citations.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect Google DriveDocs, sheets, slides, files.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, 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.