Lance Martin

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624 appearances 1 recordings 1 series first heard Sep 2025 last heard Sep 2025

Lance Martin’s voice in public audio — every appearance, attributed to the second.

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Latent Space: The AI Engineer Podcast · Context Engineering for Agents - Lance Martin, LangChain · 11 Sep 2025 podcast

But the point is you're not just plumbing all the context from your tool calls back into the agent's message history.

You're saving it an externalized system.

You're fetching it as needed.

This saves token costs significantly.

So that's the offloading concept.

This is actually a very interesting and important point.

So I'll give an example from what I did with Open Deep Research.

So Open Deep Research is a deep research agent that I've been working on for about a year.

And it's now, according to Deep Research Spence, the best performing deep research agent, at least on that particular benchmark.

So it's pretty good.

Listen, it's not as good as Open Eyes Deep Research, which uses end-to-end RL.

It's all fully open source, and it's pretty strong.

So I just do carefully prompted summarization.

I try to prompt the summarization model to give an exhaustive set of kind of bullet points of the key things that are in the post, just so the agent can know whether to retrieve the full context later.

So I think it's kind of prompting, if you're doing summarization, carefully for recall, compressing it, but like making sure that all the key bullet points necessary for the LLM to know are

what's in that piece of full context is actually very important when you're doing this kind of summarization step.

Now, Cognition had a really nice blog post talking about this as well, and they mentioned you can really spend a lot of time on summarization, so I don't want to trivialize it, but

At least my experience has been it's worked quite effectively.

Prompt a model carefully to capture exactly.

So in this post, they talk a lot about even using a fine-tuned model for performing summarization.

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