Context Engineering for AI Agents with LangChain and Manus

Join us for a deep dive into context engineering – the critical practice that determines how well your AI agents perform in production. Lance Martin from LangChain and Manus co-founder Yichao "Peak" Ji share battle-tested strategies for managing context windows, optimizing performance, and building agents that scale. Peak was recently named one of MIT's Innovators Under 35 for his work on AI agents. Here, we cover Manus's context engineering approach.

Strategies include:

(1) *Context reduction* via dual-form tool results (full/compact) with policy-based compaction and schema-driven summarization
(2) *Context offloading* through layered action spaces (function calling → sandbox utils → packages/APIs) with filesystem-based state management and shell utilities instead of vectorstore indexing
(3) *Context isolation* using minimal sub-agents (planner, knowledge manager, executor) with agent-as-tool paradigm and constrained decoding for schema-based inter-agent communication.

📊 Access the Presentations:

Lance Martin's slides (LangChain): https://docs.google.com/presentation/...

Yichao "Peak" Ji's slides (Manus): https://drive.google.com/file/d/1QGJ-...

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Chapters
0:01:00 Introduction to context engineering
0:12:00 Why context engineering in Manus
0:15:00 Context reduction in Manus
0:19:20 Context isolation in Manus
0:22:17 Context offloading in Manus
0:29:00 Avoid context over-engineering
0:31:00 Q&A: Explain sandbox utils in Manus
0:31:55 Q&A: Indexing (vectorstore) vs just using files
0:32:50 Q&A: Memory in Manus
0:34:30 Q&A: Manus and The Bitter Lesson
0:36:44 Q&A: Data format
0:37:45 Q&A: Summarization tips
0:40:00 Q&A: Sub-agents as tools
0:43:57 Q&A: Model choice
0:46:20 Q&A: Tool selection
0:49:48 Q&A: Planning
0:53:35 Q&A: Guardrails
0:55:39 Q&A: Evals
0:57:15 Q&A: Using RL