I Built 3 Lead Gen AI Agents Using ONLY My Words (beginner tutorial)

I Built 3 Lead Gen AI Agents Using ONLY My Words (beginner tutorial)

by Nate Herk

Applied Sciences & Engineering AI — Models & Research UYQArtificial intelligence (AI)UYQMMachine learning
🎙 Nate Herk 👥 964K 📅 August 11, 2025 ⏱ 15 min 👁 28K 📄 tutorial 🧭 2026-08-28
Available in: English (current) Français

Keywords

lead generationAI agentsno-codeLindyoutreach

Summary

In this tutorial, Nate Herk demonstrates how to build three AI agents using the no-code platform Lindy AI, purely through natural language instructions. The first agent generates leads by searching People Data Labs based on industry and location, storing results in a Google Sheet. The second agent enriches those leads by researching company pain points via web search and updating the sheet. The third agent crafts personalized outreach emails and saves them as Gmail drafts, updating a status column. Throughout the video, Nate shows the agent builder interface, explains the generated workflows, and troubleshoots issues like incomplete processing. He also compares Lindy to n8n, highlighting Lindy’s simplicity and context stacking. The video concludes with suggestions for further automation, such as follow-up sequences, and emphasizes the importance of understanding the underlying workflows despite the ease of building.

136 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video provides practical, actionable information for building AI agents without coding. The creator demonstrates real workflows, shows both successes and failures, and explains how to iterate on the generated agents. The argumentation is clear and logical, emphasizing the benefits of natural language building while cautioning about the need to understand the underlying logic. The comparison with n8n is brief but useful, and the creator’s experience adds credibility.

Scientific Rigor, Source Quality, Title Accuracy

The video is a tutorial with a practical focus; it does not cite scientific sources. The creator mentions People Data Labs and Lindy, but no external references are provided. The title accurately reflects the content. The description includes links to the creator’s community and n8n affiliate link, which are commercial in nature. The video’s claims about the capabilities of Lindy are based on the creator’s own testing, which is a limitation in terms of scientific rigor.

159 words

Title / Content Match

The title accurately reflects the content: the creator builds three AI agents using natural language, as promised.

Quality & Reliability

6/10

The video is a practical tutorial demonstrating the use of a no-code AI platform (Lindy) to build lead generation workflows. It provides clear step-by-step instructions and shows real results, but lacks in-depth technical explanations and independent verification of claims. The creator is transparent about limitations and errors encountered, which adds credibility.

Chapters

Cited Sources

  • n8n partner link — Affiliate link for n8n, mentioned as an alternative to Lindy.
  • Nate Herk LinkedIn — Creator's professional profile.
  • Skool community (free) — Free community for discounts and resources.
  • Skool community (paid course) — Paid course on building AI agents.

Concurring Sources

Contribution & Novelties

The video showcases a novel approach to building AI agents using natural language, which is a significant shift from traditional coding. It provides a concrete example of how no-code platforms can democratize AI automation. The creator’s emphasis on understanding the generated workflows is a valuable insight for beginners.

Pour aller plus loin :

  • Lindy AI — Official platform, relevant for exploring the tool.
  • People Data Labs — Data source used for lead generation.
  • n8n — Alternative workflow automation tool, useful for comparison.
  • AI agent — Conceptual background on AI agents.
  • Prompt engineering — Relevant for understanding how natural language instructions are processed.

102 words

Radar Profile

The radar profile shows moderate scores across all dimensions, with slightly higher scores in information quantity and reliability. This reflects a tutorial that provides useful practical information but lacks deep technical depth and independent verification.

Reliability 6/10