11–20 of 130 posts
There's also a cookbook with useful code examples: https://github.com/anthropics/anthropic-cookbook/tree/main/p...
Blogged about this here: https://simonwillison.net/2024/Dec/20/building-effective-age...
I put the agents in quotes because anthropic actually talks more about what they call "workflows". And imo this is where the real value of LLMs currently lies, workflow automation. They also say that using LangChain and other frameworks is mostly unnecessary and does more harm than good. They instead argue to use some simple patterns, directly on the API level. Not dis-similar to the old-school Gang of Four software…
Aren't you editorialising by doing so?
However the post was posted here yesterday and didn't really have a lot of traction. I thought this was partially because of the term agentic, which the community seems a bit fatigued by. So I put it in quotes to highlight that Anthropic themselves deems it a little vague and hopefully spark more interest. I don't think it messes with their message too much?
Honestly it didn't matter anyways, without second chance pooling this post would have been lost again (so thanks Daniel!)
Key to understanding the power of agentic workflows is tool usage. You don't have to write logic anymore, you simply give an agent the tools it needs to accomplish a task and ask it to do so. Models like the latest Sonnet have gotten so advanced now that coding abilities are reaching superhuman levels. All the hallucinations and "jitter" of models from 1-2 years ago has gone away. They can be reasoned on now and you…
That isn’t simple. There is a lot of nuance in tool definition.
Key to understanding the power of agentic workflows is tool usage. You don't have to write logic anymore, you simply give an agent the tools it needs to accomplish a task and ask it to do so. Models like the latest Sonnet have gotten so advanced now that coding abilities are reaching superhuman levels. All the hallucinations and "jitter" of models from 1-2 years ago has gone away. They can be reasoned on now and you…
> you simply give an agent the tools That isn’t simple. There is a lot of nuance in tool definition.
This is by far the most practical piece of writing I've seen on the subject of "agents" - it includes actionable definitions, then splits most of the value out into "workflows" and describes those in depth with example applications. There's also a cookbook with useful code examples: https://github.com/anthropics/anthropic-cookbook/tree/main/p... Blogged about this here: https://simonwillison.net/2024/Dec/20/building-…
Have been building agents for past 2 years, my tl;dr is that: Agents are Interfaces, Not Implementations The current zeitgeist seems to think of agents as passthrough agents: e.g. a lite wrapper around a core that's almost 100% a LLM. The most effective agents I've seen, and have built, are largely traditional software engineering with a sprinkling of LLM calls for "LLM hard" problems. LLM hard problems are problems…
The balance of traditional software components and LLM driven components in a system is an interesting topic - I wonder how the capabilities of future generations of foundation model will change that?
My personal view is that the roadmap to AGI requires an LLM acting as a prefrontal cortex: something designed to think about thinking. It would decide what circumstances call for double-checking facts for accuracy, which would hopefully catch hallucinations. It would write its own acceptance criteria for its answers, etc. It's not clear to me how to train each of the sub-models required, or how big (or small!) they n…
I put the agents in quotes because anthropic actually talks more about what they call "workflows". And imo this is where the real value of LLMs currently lies, workflow automation. They also say that using LangChain and other frameworks is mostly unnecessary and does more harm than good. They instead argue to use some simple patterns, directly on the API level. Not dis-similar to the old-school Gang of Four software…
I've built and/or worked on a few different LLM-based workflows, and LangChain definitely makes things worse in my opinion.
What it boils down to is that we are still coming to understand the right patterns of development for how to develop agents and agentic workflows. LangChain made choices about how to abstract things that are not general or universal enough to be useful.