AI AGENTS
Competitor Price-Change Watcher with Headless Capture and Decision Agent
On a schedule, drives a headless browser to capture competitor product pages, extracts current prices, compares against the last stored snapshot.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule fires every 4 hours
- ActionCapture competitor pages in headless browserBrowserbase
- ActionLoad last price snapshot for diffPostgres
- LogicStop if no price changed
- ActionAgent classifies move and drafts recommendationOpenAI
- ActionWrite new snapshot back to storePostgres
- OutputPost verdict to pricing channelSlack
What it does
Watches a list of competitor product URLs and tells your team when a price changes — with context, not just a number. It scrapes each page in a real headless browser (so JS-rendered prices and bot walls don't break it), diffs against the last run, and routes meaningful moves to Slack with an agent's recommendation.
When to use it
Use it when you sell against a handful of named competitors and need to react to their pricing within hours, not at the end of a quarter. Ideal for ecommerce, SaaS, and DTC pricing owners who currently check tabs by hand.
How it works
- 1A schedule fires (e.g. every 4 hours).
- 2Browserbase loads each competitor URL in a headless session and returns the rendered HTML/price text.
- 3The flow reads the previous price snapshot from Postgres and compares it to the new capture.
- 4A logic gate stops the run if nothing changed, suppressing noise.
- 5When a price moved, an OpenAI agent classifies the move (promo vs. permanent), estimates margin impact, and drafts a recommendation.
- 6The new snapshot is written back to Postgres and the verdict is posted to a Slack pricing channel.
Set it up
What you configure once, before turning it on.
- 1Connect BrowserbaseHeadless browsers, sessions, replays.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect SlackChannels, DMs, threads, mentions.
- 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
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.
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.
Datadog Bill Spike Attribution Agent
When a daily Datadog cost check detects a spend jump, an agent attributes the increase to the specific services and metric types driving it and posts a ranked breakdown to Slack.
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.
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.
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.
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.