Building an Agent in 14 Days: Why Your Code Isn't the Real Value
sujingshen · x · 2026-08-14
Recent releases like DeepSeek's open-source CLI dsh, Xiaomi's MiMo Code, and Xindong's Cindy highlight a trend: the barrier to building AI agent tools is collapsing rapidly.
The author dissects the architecture of AI agents into two distinct layers:
- Harness (Execution Layer): The engineering system outside the LLM itself, handling context management, tool calling, and sandboxing. Tools like Claude Code and DeepSeek's dsh operate here.
- Agent Client (Product Layer): The shell built on top of the harness, managing UI, cross-device sync, memory, and team permissions. Products like Cindy and Tencent WorkBuddy belong here.
The core argument is that as foundational models rapidly advance, simply writing code for the execution layer or client shell offers diminishing long-term value. Developers must pivot from merely 'writing code' to reshaping workflows and building unique business ecosystems.
Related event: DeepSeek and Xiaomi Open-Source New Agent Tools(2 posts)→
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