Manus: Context Engineering for Efficient AI Agents
The Manus project team chose to leverage the in-context learning capabilities of existing models instead of training large models from scratch when building their AI agent. The article distills four key learnings: 1. Optimize KV cache hit rate by keeping prompt prefixes stable, appending to context, and explicitly marking cache breakpoints; 2. Mask, don't remove, tools; dynamically manage tool availability to avoid cache invalidation and model confusion; 3. Use the file system as external memory for persistent, unlimited context; 4. Manipulate attention by reiterating objectives and retaining error information for learning. These practices significantly improve AI agent performance and stability, offering valuable insights for building efficient AI agents.
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