Ai production architecture
📚 Agent skills distilled from technical books — AI Engineering, Context Engineering, Designing Data-Intensive Applications, and more. Agent-agnostic, plain Markdown. Give your AI agent a bookshelf.
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Practical knowledge for architecting and operating AI applications in production. Covers the AI engineering architecture (context enhancement, guardrails, model router, gateway, caching, agent patterns), monitoring and observability (metrics, logs, traces, drift detection), pipeline orchestration, and user feedback systems (extracting conversational feedback, feedback design, biases, degenerate loops). Use this skill when: - Designing the architecture for a production AI application - Adding input/output guardrails - Setting up model routing or a gateway - Implementing caching (exact, semantic) - Building observability for an AI system - Designing user feedback collection
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AI Production Architecture
Knowledge from "AI Engineering" by Chip Huyen (Chapter 10). End-to-end production patterns for AI applications.
Quick Start
- Check
guidelines.mdto find which files to load for your task - Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
- Apply guidance to your work
Contents
References
| Category | Purpose |
|---|---|
architecture-patterns | Step-by-step architecture (context, guardrails, router/gateway, caching, agents) |
monitoring-observability | Metrics, logs/traces, drift detection, pipeline orchestration |
user-feedback | Extracting conversational feedback, feedback design, biases, degenerate loops |
Workflows
| Task | Workflow |
|---|---|
| Build production AI architecture (5-step process) | workflows/build-production-architecture.md |
| Set up observability (metrics, logs, traces, drift) | workflows/setup-observability.md |
Guidelines
See guidelines.md for task-based file selection.