Outbound SDR agent that researches, personalizes, and follows up across email, LinkedIn, and SMS — and classifies replies.
A human SDR sends 30-60 personalized emails a day before quality drops. The bottleneck isn't writing, it's research: figuring out who to contact and what hook works. An AI SDR stack (Clay for enrichment + Smartlead for sending + Lindy/n8n for orchestration) can run thousands of personalized sequences with reply classification — escalating only positive responses to a human. The pattern is now table-stakes for B2B agencies and any service firm with a defined ICP.
Specifics: industry, company size, role, geography, intent signals (hiring for X, just raised, using competitor tool). The narrower, the better the personalization downstream.
Source list from Apollo / LinkedIn Sales Navigator. Enrich each row with website summary, latest news, mutual connections, and a 1-line 'why now' hook generated by GPT-4 grounded in the enrichment data.
Prompt the model with the enrichment payload. Output: 35-55 word email with one specific personalization line, one value prop, one soft CTA. Reject any email that doesn't pass a personalization check (unique line not present in 50%+ of sends).
Day 0 email → Day 3 LinkedIn connect (no note) → Day 5 follow-up email → Day 10 LinkedIn message → Day 14 break-up email. Throttle to 30 sends/day per inbox to protect deliverability.
Every reply gets routed to a classifier: positive (book meeting), question (auto-draft answer for human review), not now (move to nurture), unsubscribe (DNC). Only positives + complex questions hit a human.
When the classifier sees a yes, the agent inserts a Calendly link OR proposes 3 specific times pulled from the rep's calendar. Confirms via reply, drops a calendar invite.
Monitor open rates, reply rates, spam-folder placement, sender reputation. Cool an inbox if open rate drops below 30%. Rotate inboxes and warm new ones continuously.
Use these templates as-is or customize for your business.
You are writing a first-touch outbound email. Use this enrichment data: {{enrichment_json}}. Constraints: 35-55 words, one specific personalization line drawn from the data (not generic flattery), one value prop tied to a likely pain point for their role, one soft CTA (a question, not a meeting ask). Tone: peer-to-peer, no jargon, no exclamation marks. Output only the email body — no subject line, no signature.Classify this reply into exactly one bucket: POSITIVE (interest, asking to learn more, asking for time), QUESTION (specific question that needs an answer before they'll commit), NOT_NOW (interested but bad timing), NOT_INTERESTED (polite no), UNSUBSCRIBE (asked to be removed), OOO (out of office). Reply text: {{reply_body}}. Output JSON: {"label": "...", "confidence": 0-1, "suggested_action": "..."}Hi {{first_name}} — circling back one last time. If now isn't the right moment for {{value_prop}}, totally understood. I'll close the loop on my end. If anything changes, you have my email. — {{sender_first_name}}Get a new AI workflow every week. Prompts, tool stacks, and ROI math included.
Multiple specialized agents collaborate: a router/orchestrator delegates to sub-agents (researcher, writer, classifier). Higher capability, more failure surface — invest in observability before scaling.
Learn the agentic glossary →Where this workflow tends to break in production — and what to put in place before you ship it.
Spam folder placement kills the campaign
Mitigation: Daily seed-list deliverability tests; cool inboxes that drop below 90% inbox placement.
Personalization line is generic
Mitigation: Programmatic dedup check against last 100 sends; reject if Jaccard similarity > 0.6.
Do not run multi-channel outbound without monitored deliverability infrastructure (separate domain, warmed inboxes, SPF/DKIM/DMARC). Do not use for industries where cold email is restricted (healthcare, regulated finance to consumers). Skip if you can't handle 30+ inbound conversations/month — the bottleneck moves to your closer.
A phased approach to get this workflow running and delivering ROI.
Days 1–30
Foundation
Days 31–60
Optimization
Days 61–90
Scale
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