Context Engineering for AI Agents: Lessons from Building Manus

Manus adopted context engineering over end-to-end model training because in-context learning enables rapid iteration cycles (hours instead of weeks) and keeps the product independent of underlying model improvements. The team discovered that KV-cache hit rate is the critical production metric for AI agents due to their unique token ratio (100:1 input-to-output for Manus), where maintaining stable prompt prefixes can yield 10x cost savings compared to uncached inference. This approach required iterative architecture refinement through empirical experimentation, which they humorously term "Stochastic Gradient Descent."

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