AI AGENTS
Self-Critiquing Competitor Teardown Drafter
An agent researches a named competitor across the web, drafts a structured teardown.
How it runs
The automated pipeline, trigger to output.
- TriggerOperator submits competitor name + analysis angle
- ActionSearch primary sources with ExaExa
- ActionDraft structured teardown with OpenAIOpenAI
- LogicSelf-critique pass: flag unsupported or overstated claims
- ActionRevise draft, downgrading unsupportable claimsOpenAI
- OutputPublish reviewed draft to Notion review pageNotion
What it does
You name one competitor; the agent returns a teardown that has already survived its own red-team. It gathers evidence, writes the analysis, then critiques that analysis for unsupported claims and bias, revising before a human ever sees it.
When to use it
When your team keeps shipping competitive briefs that overstate weaknesses or cite thin sources. Use it to get a defensible first draft that an analyst edits in 20 minutes instead of writing from scratch.
How it works
- 1You submit a competitor name and the angle you care about (pricing, positioning, product gaps).
- 2The agent runs Exa neural search to pull primary sources — docs, pricing pages, reviews, recent news.
- 3It drafts a structured teardown: overview, strengths, weaknesses, threats to us.
- 4A self-critique pass re-reads the draft against the sources and flags every claim that lacks a citation or overstates the evidence.
- 5The agent rewrites weak claims, downgrading or cutting anything it cannot support.
- 6The reviewed draft, with a confidence note per section, is published to a Notion review page tagged for the human owner.
Set it up
What you configure once, before turning it on.
- 1Connect ExaNeural search across the web.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect NotionPages, databases, comments.
- 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.
