Built a robust outbound system
Built a full agentic outreach engine — complex logic, multiple data sources, edge-case handling. My n8n version took months. Rebuilt it in Antigravity: 47 minutes to a working system, a week to harden it, same-or-better results. No hype. Just reality-shifted execution. pain → outcome: Pain: SDRs wasting 10–20 hours/week on copy-paste, bad signals, and manual personalization. Fix: Replace repetitive operator work with an agentic workflow that plans, reasons, and self-corrects. Outcome: Projects that were 20–40 hours become 2 hours. Multiple tools collapse into one system. More output, same headcount. How the change actually shows up (practical bullets): More calls: personalization at scale (real context, not templates) → reply and booking lift. Less ops drag: data entry, enrichment, routing automated — ops time cut dramatically. Fewer tools: one agentic system chains research → outreach → follow-up → triage. Resilience: system can self-correct and improve iterations without you babysitting each run. Reality check (be brutally honest): AI isn’t perfect. It still makes mistakes. But the speed of iteration now means you fix errors fast and gain leverage far quicker than you did last year. Ignoring AI isn’t neutral — it’s falling behind. The winners will be teams that treat AI like an operator, not an assistant. Quick blueprint (what to build first): Signal layer — scrape hiring, posts, fundraising, product updates. If they signal, they’re worth outreach. Context layer — pull recent content (LinkedIn posts, blog, news) and compress to a 1-line icebreaker. Agent logic — rules for sequencing channels, cadence, and self-correction on failures. Quality layer — automated checks: validate emails, detect tone problems, flag risky sends. Measurement — bookings, replies, time saved per SDR. Optimize for bookings and pipeline, not opens. Agency owner: stop wasting 2 hours/day per rep on research. That can become 10× more leads qualified without hiring.