This is a new paradigm for interacting with Claude that is significantly more "inline" with all the other human activity org-wide. Once you do all of the under the hood engineering work to make this "just work" (e.g. across tools, integrations, compute environments, memory, security, etc.), Claude basically joins the team in a seamless way - you can talk to it as you would talk to a person and it can help with a very large variety of workloads.
Imo this is the 3rd major redesign of LLM UIUX. The first paradigm was that the LLM is a website you go to, the second was that it is an app you download to your computer. This third one is that it is a self-contained, persistent, asynchronous entity with org-wide tools and context, working alongside teams of humans. It really takes a while to wrap your head around it, but it works and it is awesome.
Had way too much runway left on my plan last night (which brings its own weird anxiety), so I spent some time with Fable - driven through Alan, the agent harness I've been building at @apideck. A few takeaways: 🧵
Today WorkOS is launching auth.md
An open protocol for agents to register for services on the web.
We're partnering with @Cloudflare and @Firecrawl as some of the first providers.
Why did we build this? And why now? 🧵
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.
The harness is no longer a wrapper around the model. The harness is part of the model's effective parameters. The post training process embeds the harness's tool surface, schema shapes, memory rituals, citation contracts, and system prompt structure into the model's instinct set.
You can take the weights to a different harness, but you cannot take the instincts. The instincts only fire when the harness presents the world the way the post training presented it.
Also, the matched pair is not static. The right harness for a model in March is not the right harness for that model's successor in October!
Once again, if you want to stay at the edge, you have to delete most of your code when a new model is released... LLMs eat scaffolding for breakfast!
Every abstraction shift in software history made devs more productive by raising the level of intent.
This is the next step: from writing code to orchestrating systems that write code (building "the factory" for your code).
The unsolved problem isn't generation but verification. That's where engineering judgment becomes your highest-leverage skill.
To truly scale, think "factory model" - orchestrate fleets of agents like a production line: clear specs as blueprints, TDD for quality control, strong architecture to amplify leverage.