How do I build an automated competitor monitoring agent?

Build an automated competitor monitoring agent in three pieces: (1) a fetch layer (AgentFetch) that polls competitor URLs on schedule and returns clean markdown; (2) a diff layer that compares today's markdown to yesterday's and surfaces changes; (3) a model layer that summarizes meaningful changes and pushes alerts via email/Slack/Discord. Concrete stack: a Python script on a cron or Railway/Render scheduler that calls AgentFetch's fetch_url against 10-30 competitor URLs (pricing pages, blog index, careers page, changelog, status page), stores markdown in S3/SQLite, runs difflib against the previous snapshot, and sends pages with >50-character diffs through Claude Haiku for "is this meaningful?" filtering. Total cost: ~$5-15/month for the AgentFetch paid tier + ~$1-3/month in Haiku tokens for a 20-competitor monitor running every 6 hours. Why use AgentFetch vs raw requests: pages change layout regularly — nav menus, footer links, A/B tests — and raw HTML diffs are 90% noise. Clean markdown diffs catch real content changes (new pricing tier, new blog post, new exec hire) while ignoring HTML churn. Pair with the extract_json tool against pricing pages to detect price changes as structured data: a $5 → $7 plan change is far more actionable as JSON than as a 1,000-line HTML diff.