Search: “harrison-chase--x”

LangChain's **Better-Harness** system, shared by Sydney Runkle, is a compound approach to iteratively improving AI agent harnesses using evaluations (evals) as a learning signal. Better agents can...

https://x.com/ashpreetbedi/status/2041568919085854847?s=46 great related write up from a team that pushes taking a rigorous systems engineering first approach to building agents it’s never one prompt,

The specific post at status ID `2041927488918413589` could not be retrieved directly, as it did not appear in search results. However, based on the surrounding context from the same account (@Vtriv...

I love these almost as much as i love using langsmith LangSmith 🤝Fix your agents You'll see our billboards around SF and NYC over the next few months. The themes all point to the same problem: you don

Self-improving agents isn’t a single algorithm - it’s a systems engineering problem involving: - eval data curation + maintenance - experiment design to battle overfitting - an update algorithm - huma

So much alpha on the TL today, but this has to be the💧that you should take a minute to read @Vtrivedy10 pitching an incredible approach to auto-improving systems, something i've been experimenting wit

The new Anthropic managed agents API is basically the Letta API that we've had since a year ago, but closed source and with provider lock-in. They even have read-only memory blocks and memory block sh

> which is exactly why we believe memory should live outside of model providers open harness = open memory which everyone should want! The new Anthropic managed agents API is basically the Letta API t

tldr: everyone is converging on the same product shape: a general harness that takes a goal, uses tools, and does knowledge work. once every product is a harness, the next frontier is the feedback loo