12-factorアプリの原則を、スケーラブルで保守しやすいエージェントシステム向けに適応させた本番運用可能な方法論で、包括的なモニタリングと評価を備える。
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パターン: twelve-factor app の原則を、スケーラブルで保守しやすいエージェントシステム向けに適用した本番運用向け方法論
理由: エージェントを 70-80% のプロトタイプ品質から、human-in-the-loop を備えた本番水準のシステムへと引き上げる
重要なポイント: 最も成功しているエージェントは最も「エージェント的」なものではなく、制御された変換のために LLM を活用する、よく設計されたソフトウェアシステムである
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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