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# How Benchling builds agents when the smartest AI isn't smart enough

**[LangChain](https://daily.dev/sources/langchain)** · 4 min read · 0 upvotes · 0 comments

## Summary

Benchling's Head of AI Nicholas Larus-Stone shares how the R&D data platform builds AI agents for scientific work. Key insights include using multiple model providers on the same task to catch errors through disagreement, a structured weekly trace review process with a rotating 'fire chief', and how agents are compressing scientific workflows by reducing dead time between experiment steps. The conversation also covers context engineering, memory systems where agents create their own skills, evals without clean benchmarks, and why understanding LLMs is more analogous to biology than software engineering.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.langchain.com/blog/benchling-max-agency-podcast>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#langchain](https://daily.dev/tags/langchain)

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