These AI Marketing Agents Get You Customers

Cody Schneider frames marketing agents as the direct successor to coding agents, arguing that just as coding agents made software development on demand, marketing agents now make customer acquisition on demand. Every major marketing channel is currently degraded because AI-generated content has flooded them, which has pushed down cold email reply rates and raised the value of higher-quality behavioral targeting signals.

The first agent monitors LinkedIn posts from a manually curated list of 10 to 20 niche influencers, extracts people who liked or commented, and treats that engagement as a hand-raise signal indicating genuine interest in a topic. Schneider argues this behavioral signal is more valuable than firmographic or demographic data alone. A handful of outlier creators in any niche capture roughly 80 percent of industry engagement, so monitoring only those accounts is sufficient and chasing the long tail is not worth the effort. The technical layer uses Appify, a scraping API covering LinkedIn and other platforms through a single key, with the API Maestro for LinkedIn endpoint preferred for stability. One example pull from a single LinkedIn post returned 63 raw contacts before deduplication, and the agent runs on a daily cron job to catch net new posts automatically.

Before enrichment, the agent assesses each person and their company for ICP fit and only proceeds if fit criteria are met. The waterfall enrichment sequence starts with Get Leads dot io, falls to Apollo, then to Origami, which aggregates multiple providers so a single LinkedIn URL can be run through all of them automatically. Lead Magic handles mobile phone number enrichment. From 50 LinkedIn URLs, a representative run might yield 32 emails from Get Leads, 10 more from Apollo, and the remaining 8 from other tools, achieving roughly an 80 percent find rate overall. Million Verifier then classifies emails as good, risky, or bad before sending, because mailing invalid addresses damages deliverability. Obtaining emails through these data brokers is described as legal and on the white hat end of the spectrum in the United States, though CAN-SPAM rules apply to subsequent use and EU compliance rules differ significantly.

Cold email infrastructure uses burner domains and inboxes to protect the core business domain, with separate domains maintained for cold email, email marketing, transactional email, and core business email. Approximately 10,000 cold emails can be sent per month for about 100 dollars in infrastructure costs using HyperTide, Instantly AI costs approximately 97 dollars per month to start, and total infrastructure to begin is roughly 200 dollars per month. Instantly AI has an API and webhooks that allow an agent to monitor inboxes, write personalized copy, push campaigns, respond to positive replies, and re-engage leads that went cold every six months. Integration with scheduling tools like Calendly or cal.com lets the agent verify whether outreach produced a booked discovery call. The full system is described as an SDR in a box.

A co-founder identified as Max holds that the only true agent is a coding agent and that everything else is software produced by that coding agent. The broader view expressed is that agent frameworks are often unnecessary bloat for finite, well-defined marketing automation problems. Using inference tokens for tasks that could be handled by static code is described as wasteful and as increasing the likelihood of failure. Giving an LLM autonomous control over something like a Facebook ads account risks destroying the account, whereas custom rule-based software produces higher quality outcomes.

The second agent handles LinkedIn organic content by interviewing team members, transcribing those conversations, extracting insights, and writing and auto-scheduling posts via a tool called Ordinal MCP. Prompting an agent to write LinkedIn content without source material produces generic low-quality output, and LinkedIn released an AI slop detection feature the morning of the recording. Real human conversation, sales calls, Gong transcripts, Slack channels, Notion, and internal codebases are all cited as valid source material because they contain original ideas that generic LLM generation cannot reproduce. Ordinal supports multiple connected LinkedIn accounts, allows those accounts to interact with one another, and surfaces analytics data that feeds back into the agent to shape future content cycles. Schneider claims effective content strategy relies on reposting proven high-performing posts approximately every 90 days rather than generating entirely new ideas, and says he has posts that have reliably gone viral on that cadence for two years.

LinkedIn's average cost per thousand impressions is approximately 22 dollars, and even an account with 500 followers generating roughly 1,000 impressions per post captures the equivalent of about 20 dollars in earned media value per post. Topic-based or theme-based pages are presented as an alternative to personal brand accounts for generating inbound leads, with Julian Shapiro cited as an example of building an account called at growth tactics rather than a branded Demand Curve account in order to attract a growth-interested audience and funnel them toward his agency.