building-agents

# Building production LLM agents (model-agnostic) A thin provider adapter, a disciplined agent loop, schema-validated tools, provider-neutral RAG, eval gates, OTel tracing, and optionally an MCP server — so swapping OpenAI ↔ Anthropic ↔ Gemini ↔ OSS is a config change, not a rewrite. ## The one rule > Program against a **capability interface**, never a vendor SDK. Vendor specifics (model id, tool-schema shape, JSON mode, caching, token limits) live behind one adapter resolved from config. Model names and prices rot — if one appears in business logic it's a bug, and re-verify the dated tables before quoting a number. **Hand off instead when:** a new non-trivial feature has no approved spec + plan under `02-DOCS/wiki/sdd/` → stop and run [`specify`](../specify/SKILL.md) first (method: [`sdd`](../sdd/SKILL.md)), which routes back here once the plan is approved; one-line/low-risk changes go straight through. Anthropic-SDK internals (caching, thinking, batch) in a file that *only* imports `anthropic` → `claude-api` if your environment has it, since this skill stays multi-provider. Workspace scaffolding → [`harness`](../harness/SKILL.md). Choosing *which* coding agent to use → agent-eval territory. Pure prompt-wording tuning with no architecture change → prompt engineering, not this. A one-shot throwaway prompt, or no retrieval/tools/loop/evals at all → you don't need an agent; call the SDK directly and say so. ## Decision rules (read before writing code) 1. **Adapter first**...