The Digital Press

Manus AI shares lessons from building their agent system, focusing on how careful context engineering can improve performance, cost, and reliability in AI agents. They highlight practical strategies like stable prompts, masking tools instead of removing them, and using the file system as external memory.

Why it matters: Proper context design dramatically reduces latency and inference costs while improving agent stability and error recovery.

The big picture: Context engineering enables agents to scale and adapt without retraining, making them more flexible amid rapid model improvements.

Stunning stat: Manus’s input-to-output token ratio is about 100:1, making efficient KV-cache usage critical for cost and speed.

Commenters say: Readers appreciate the practical use of file systems as memory, praising its simplicity and effectiveness without complex backends.