Context Engineering for Agents - Lance Martin, LangChain
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What is the episode’s introduction and who are the hosts and guest?
Hey everyone, welcome to the Laden Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swix, founder of Small AI.
Hello, hello. Uh we are so happy to be in the remote studio with Lance Martin from Langchain Langgraph and everything else he does.
Welcome. It's great to be here. I'm a longtime listener to the pod and uh is finally great to be on.
You've been part of uh you know our our orbit for a while. You you spoke at uh one of the AIEs and also obviously we're we're pretty close with with Langchain. Recently though, you've also like been doing a lot of tutorials. I remember you did like R one Deep Researcher, which is a pretty popular project, and you think ambient agents. But the thing that really sort of prompted me to reach out and say, like, Okay, it's finally time for the Lance Martin pod is your recent work on context engineering, which is Alder Age. Uh how'd you get into it?
Well, you know, it's funny. Buzzwords emerge oftentimes when people have a shared experience. And I think Lots of people started building agents kind of early this year, mid this year, quote unquote the year of agents. And I think what happened is when you put up put when you kind of put together an agent, it's just tool calling a loop, it's relatively simple to lay out, but it's actually quite tricky to get to work well. And in particular, managing context with agents is a hard problem. Karpathi put out that tweet canonizing the term context engineering. And he kind of mentioned this nice definition, which is context engineering is the challenge of feeding an LM just the right context for the next step, which is highly applicable to agents.
And I think that really resonated with a lot of people. I in particular had that experience over the past year working on agents. And I wrote about that a little bit in my piece talking about building open deep research over the past year. So I think it was kind of an interesting point that the term capture a common experience that many people were having and it took hold because of that.
How do you define the lines between prompt engineering and like context engineering? So is uh prompt optimization like context engineering in your mind? Like I think people are confused. Like, are we replacing the term? Like what w what is it?
Well, I think that, you know, prompt engineering is kind of a subset of contact engineering. I think when we kind of move from chat models and chat interactions to agents, there was a big shift that occurred. So with chat models, working with chat GPT, the human message is really the primary input. And of course, a lot of time and effort is spent in crafting the right message that's passed to the model. With agents, the game is a bit trickier though, because the agent's getting context not just from the human, but now context is flowing in from tool calls during the agent trajectory. And so I think this was really the key challenge that I observed, and many people observed, is like, oof, when you put together an agent, you're not only managing, of course, the system instructions, system prompt, and of course user instructions.
You also have to manage all this context that's flowing in at each step over the course of a large number of tool calls. And I think there's been a number of good pieces on this. Madness put out a great piece talking about context engineering with Madness. And they made the point that the typical menace task was is like 50 tool calls. Anthropics multi-agent research is another nice example of this. They mentioned that the typical production agent, and this is probably referring to Claude Code, could be other agents that they've produced, is like hundreds of tool calls. When I had my first experience with this, I think many people have this experience, you put together an agent, You're sold the story that's just tool calling in a loop.
That's pretty simple. You put it together. I was building deep research. These research tool calls are pretty token heavy. And suddenly you're finding that my deep researcher, for example, with a naive tool calling loop was using 500,000 tokens.
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Chapters
8 chapters
1
What is the episode’s introduction and who are the hosts and guest?
0:04–7:48
2
How does Lance define “context engineering” and why did the term emerge?
7:48–16:49
3
What strategies does Lance suggest for off‑loading and summarizing context in agents?
16:49–24:27
4
How do multi‑agent architectures affect context isolation and communication?
24:27–32:03
5
What are the different retrieval (RAG) approaches used by code agents?
32:03–39:04
6
How can caching and MCP servers reduce latency and token costs?
39:04–46:52
7
What is the relationship between memory systems (LangMem) and context engineering?
46:52–53:59
8
What is the “bitter lesson” for AI engineering and how should developers adapt?
53:59–57:21
Speakers
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