Context Engineering for Agents - Lance Martin, LangChain
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What is context engineering and why did it become a buzzword in AI agents?
Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swix, founder of SmallAI.
Hello, hello. We are so happy to be in the remote studio with Lance Martin from LandChain, LandGraph, and everything else he does. Welcome. It's
great to be here. I'm a longtime listener of the pod and it's finally great to be on.
You've been part of, you know, our orbit for a while. You spoke at one of the AIEs. And also, obviously, we're pretty close with Langchain. Recently, though, you've also been doing a lot of tutorials. I remember you did like R1 Deep Researcher, which is a pretty popular project, and Async 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 all the rage. 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 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 it to work well. And in particular, managing contacts with agents is a hard problem. Karpathy 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 captured 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 context engineering? So is the prompt optimization context engineering in your mind? I think people are confused. Are we replacing the term? What is it?
Well, I think that prompt engineering is kind of a subset of context engineering. I think when we kind of move from chat models and chat interactions to agents, there's a big shift that occurred. So with chat models, working with ChatGPT, 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 agents get in 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. Manus put out a great piece talking about context engineering with Manus. And they made the point that the typical Manus task is like 50 tool calls. Anthropix Multi-Agent Research is another nice example of this. They mentioned that the typical production agent, and this is probably referring to Cloud Code, could be other agents that they've produced, is like hundreds of tool calls. When I had my first experience with this, and 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. It was like $1 to $2 per run.
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Chapters
8 chapters
1
What is context engineering and why did it become a buzzword in AI agents?
0:04–7:17
2
How does context engineering differ from traditional prompt engineering?
7:17–15:51
3
What are the five categories of context engineering and how are they applied?
15:51–23:03
4
How can off‑loading and summarization reduce token usage in long‑running agents?
23:03–30:10
5
When should you use multi‑agent architectures versus a single agent?
30:10–36:34
6
What are the trade‑offs between classic RAG pipelines and agentic search for retrieval?
36:34–43:22
7
How do caching, pruning, and memory management impact performance and reliability?
43:22–51:45
8
What is the “bitter lesson” for AI engineering and how does it guide future system design?
51:45–57:21
Speakers
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