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Context Engineering for Agents - Lance Martin, LangChain

Shownote

Lance: https://www.linkedin.com/in/lance-martin-64a33b5/ How Context Fails: https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html How New Buzzwords Get Created: https://www.dbreunig.com/2025/07/24/why-the-term-context-engineering-matters.html Content Engineering: https://x.com/RLanceMartin/status/1948441848978309358 https://rlancemartin.github.io/2025/06/23/context_engineering/ https://docs.google.com/presentation/d/16aaXLu40GugY-kOpqDU4e-S0hD1FmHcNyF0rRRnb1OU/edit?usp=sharing Manus Post: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus Cognition Post: https://cognition.ai/blog/dont-build-multi-agents Multi-Agent Researcher: https://www.anthropic.com/engineering/multi-agent-research-system Human-in-the-loop + Memory: https://github.com/langchain-ai/agents-from-scratch - Bitter Lesson in AI Engineering - Hyung Won Chung on the Bitter Lesson in AI Research: https://www.youtube.com/watch?v=orDKvo8h71o Bitter Lesson w/ Claude Code: https://www.youtube.com/watch?v=Lue8K2jqfKk&t=1s Learning the Bitter Lesson in AI Engineering: https://rlancemartin.github.io/2025/07/30/bitter_lesson/ Open Deep Research: https://github.com/langchain-ai/open_deep_research https://academy.langchain.com/courses/deep-research-with-langgraph Scaling and building things that "don't yet work": https://www.youtube.com/watch?v=p8Jx4qvDoSo - Frameworks - Roast framework at Shopify / standardization of orchestration tools: https://www.youtube.com/watch?v=0NHCyq8bBcM MCP adoption within Anthropic / standardization of protocols: https://www.youtube.com/watch?v=xlEQ6Y3WNNI How to think about frameworks: https://blog.langchain.com/how-to-think-about-agent-frameworks/ RAG benchmarking: https://rlancemartin.github.io/2025/04/03/vibe-code/ Simon's talk with memory-gone-wrong: https://simonwillison.net/2025/Jun/6/six-months-in-llms/

Highlights

In this podcast, experts delve into the evolving landscape of AI agent development, focusing on the nuances of context engineering, retrieval methods, and the frameworks that enable scalable systems. The discussion brings together insights from industry leaders and researchers, exploring the practical challenges and innovative solutions shaping the future of AI systems.
07:44
Carefully prompted summarization is crucial for recall and compression in deep research agents.
10:38
Multi-agents work well for deep research with parallelizable tasks but pose challenges in coding due to conflicting outputs.
16:23
lm.txt with good descriptions outperformed other methods in LangChain documentation retrieval.
24:21
Manus suggests context offloading as a solution to avoid irreversible pruning risks.
32:15
Caching prior message history can significantly reduce latency and cost in LangChain
42:03
Hyung Won Chung's Bitter Lesson emphasizes removing structure after initial compute investment.
52:30
Using LangGraph improved checkpointing and state management in Open Deep Research workflows.
57:00
An open-source deep research agent shows good results and is expected to improve with GPT-5.

Chapters

What Is Context Engineering and Why Does It Matter for AI Agents?
00:00
Why Multi-Agent Systems Work Well in Research but Struggle in Coding
10:38
How lm.txt Files Are Changing the Game for Code Documentation Retrieval
16:23
Can Summarization and Compaction Save Us from Context Window Limits?
24:21
How Do Buzzwords Like 'Context Engineering' Become Industry Standards?
29:46
Caching and Memory: Solving ContextRot and Compaction in Real-World AI
34:48
Building a Deep Research Agent: Adapting to Rapid Model Improvements
44:49
Why Standardized Frameworks Are Essential for Large-Scale AI Development
54:42

Transcript

Alessio: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by swyx, founder of Small AI. swyx: Hello, hello. We are so happy to be in the remote studio with Lance Martin from LangChain, LangGraph, ...