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Why Narrow AI Agents Will Win (and How I’m Building 15 Agents to Run Growth) After two weeks building and testing hundreds of AI agent workflows with Open Claw, Manus, Claude Code, and Perplexity Computer, the speaker concludes that companies will rely on teams of narrow, goal-driven agents rather than one general “command center” agent. Perplexity Computer and Manus spin up a cloud computer per task, but the speaker prefers Open Claw: a single agent on a computer with structured, extensible skills, strong memory, and messaging access via tools like Telegram, Slack, and Discord. Adding too many skills reduced reliability, so the proposed sweet spot is 7–10 skills per agent. Examples include a focused YouTube content agent optimizing for subs, views, and conversions, plus a journal agent that logs activity to Notion to inform other agents like a newsletter agent optimizing open rate and click-through. Narrow agents are easier to duplicate, share, evaluate, and automate via predictable loops. 00:00 Intro 00:48 Perplexity Computer and Manus 02:43 OpenClaw 03:52 Too many Skills to my first AI Agent 06:09 People want an employee 07:35 Testing Narrow AI Agents 08:33 Narrow Agent Example (YouTube Agent) 11:32 A Team of Narrow Agents... Why? 16:27 In Summary