Context Engineering for AI Agents: Engineering Lessons from Building Manus
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helloericsf
Fresh out the oven, still warm. Top of the tray.
Summary
The article, written by Yichao 'Peak' Ji, discusses the engineering principles behind building Manus, an AI agent platform that was acquired by Meta. It focuses on "context engineering" — the practice of designing how context is structured, retrieved, and utilized by AI agents to improve performance. The author shares lessons from Manus's development, including strategies for prompt design, memory management, tool use, and iterative optimization (analogous to SGD in machine learning). The piece serves as a technical post-mortem and guide for developers building their own AI agents.
Key quotes
· 3 pulledAt the very beginning of the Manus project, my team and I faced a key decision: should we train an end-to-end model or engineer the context?
This post shares the local optima Manus arrived at through our own 'SGD'. If you're building your own AI agent, we hope these principles help you converge faster.
Context engineering is not about prompt hacks — it's about systematically designing how information flows into and through an AI agent.
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