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