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Eugene Yan

What Eugene Yan thinks about AI agents

@eugene-yan · 15 positions · On ppll ↗

Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.

Eugene Yan did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

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Only Eugene Yan on AI agents. See everyone on AI agents

4 dated positions, 2026, in their own words. Our reading of what Eugene Yan has said — not written or endorsed by them.

4 positions so far — this page is not yet offered to search engines.

  1. Our readingThe surrounding system framework and scaffolding matter more for AI agent capability than the underlying model.

    Their wordsThe experiments showed that the system framework matters far more than the underlying model.

    ↗Patterns for Building Cybersecurity Evalseugeneyan.com

  2. 7 weeks earlier

  3. Our readingThe practices that make someone effective at working with an AI model are the same practices that make someone effective at onboarding any new human collaborator.

    Their wordsnone of this is specific to AI. It's simply how you onboard and work with any new collaborator.

    ↗How to Work and Compound with AIeugeneyan.com 1st of 3 in this piece

  5. Our readingDelegating a task to an AI agent only works if the output can be verified, which requires defining success criteria first.

    Their wordsYou can't delegate what you can't verify, so this requires first defining success criteria and metrics.

    ↗How to Work and Compound with AIeugeneyan.com 2nd of 3 in this piece

  7. Our readingAs AI agents take on bigger delegated tasks, the human bottleneck shifts to writing specs and reviewing output fast, hollowing out the middle of the work.

    Their wordsThe bottleneck has shifted from doing the work to writing clear specs and reviewing outputs fast enough to keep the pipeline moving-the middle is hollowing out.

    ↗How to Work and Compound with AIeugeneyan.com 3rd of 3 in this piece