Context Engineering for AI Agents: Lessons from Building Manus

At the very beginning of the Manus project, my team and I faced a key decision: should we train an end-to-end agentic model using open-source foundations, or build an agent on top of the in-context learning abilities of frontier models? Back in my first decade in NLP, we didn't have the luxury of that choice. In the distant days of BERT (yes, it's been seven years), models had to be fine-tuned—and

[oinume]

難しい

[stealthinu]

Agentシステム作るときに重視すべき点。KVキャッシュを重視するとこの指摘とかLLMのプロンプトキャッシュが使えるようになったのがエージェントに大きく影響していることがわかる。

[misshiki]

“Manusはコンテキストエンジニアリングに賭ける。”