将 12-factor 应用原则适配到可扩展、可维护的智能体系统的生产就绪方法论，并具备全面的监控与评估。
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模式： 将 twelve-factor app 原则应用于可扩展、可维护智能体系统的生产级方法论
原因： 把智能体从 70-80% 的原型可靠性推进到具备人在回路工作流的生产级系统
关键洞察： 最成功的智能体并不是最「智能体化」的，而是善用 LLM 完成受控转换的、工程质量过硬的软件系统
Core LLM superpower: converting natural language to structured data. The ability to take a string and turn it into JSON.
Production quality requires hand-crafted prompts, not abstractions. Own your prompts to tweak token order and system/user roles as models change.
Don't blindly append; actively manage what the LLM sees. Own the context window, squeezing traces and error summaries for self-healing.
Demystify "tool use" as simple routing. Treat tools as structured JSON outputs validated through switch statements.
Agents = prompt + switch + context + loop. Keep control-flow in code with explicit OODA loops and convergence heuristics.
Enable pause/resume and horizontal scaling. Persist execution state for idempotent restarts.
Different lifecycles, different needs. Expose launch/pause/resume endpoints for safe replay runs.
Not an edge case, but core functionality. Route high-stakes steps to humans as first-class tool calls.
Email, Slack, Discord — multi-channel by design. Trigger agents from wherever users already work.
3-10 steps max for reliability. Build small, single-purpose agents instead of chatty monoliths.
Process errors intelligently, not blindly. Compact errors into the next prompt to close the feedback loop.
Engineer reliability where models almost succeed. Find what's at the boundary of model capability and make it reliable.
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