Dexter Horthy
Founder, HumanLayer
San Francisco, California, United States
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Hey - I'm Dex, and I'm hacking on safer more reliable agents at HumanLayer. HumanLayer helps AI builders create agents that feel more like real coworkers - taking them out of ChatGPT-style interfaces and deploying them into slack, email, or wherever their users already are. Before this I was working on AI Agents that managed SQL warehouses, and did a long stint at replicated.com helping the worlds best software teams deliver Kubernetes apps into customer environments. I've been coding since 17, when I built tools for NASA researchers to navigate the south pole of the moon. Enjoyer of tacos and burpees (not necessarily in that order)
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12 Factor Agents - Principles of Reliable LLM Applications
Hi, I'm Dex. I've been hacking on AI agents for a while.
I've tried every agent framework out there, from the plug-and-play crew/langchains to the "minimalist" smolagents of the world to the "production grade" langraph, griptape, etc.
I've talked to a lot of really strong founders who are all building really impressive things with AI. Most of them are rolling the stack themselves. I don't see a lot of frameworks in production customer-facing agents.
I've been surprised to find that most of the products out there billing themselves as "AI Agents" are not all that agentic. A lot of them are mostly deterministic code, with LLM steps sprinkled in at just the right points to make the experience truly magical.
Agents, at least the good ones, don't follow the "here's your prompt, here's a bag of tools, loop until you hit the goal" pattern. Rather, they are comprised of mostly just software.
So, I set out to answer:
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Dexter Horthy
Founder, HumanLayer
San Francisco, California, United States
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