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Claude Sonnet 4.6 is the default model for most 2026 production workloads: it covers 80% of traffic with quality indistinguishable from Opus 4.7 in blind tests, at roughly 60% of Opus per-token price. Opus is still needed for complex reasoning and agentic coding on large codebases.
AI agents fail in production, and what matters is how you respond in the first twenty minutes. This runbook covers severity classification, isolating before investigating, purging contaminated memory, communicating without inventing facts, and turning every incident into a regression test before closing it as done.
Reliable agents come from measurement, not from better models or prompts. A production agent evaluation setup starts with a golden dataset of 30 to 200 cases, split roughly 60 percent normal usage, 30 percent edge cases and 10 percent adversarial, and it never uses the same model as both worker and judge.
An Agent OS is a runtime layer built to run AI agents rather than ordinary applications, and after six months of production deployments the trade-off is clear. A dedicated agent stack starts slower but stays stable; Kubernetes with orchestration bolted on top moves faster early, then hits observability and policy limits. It pays off from five active agents.
By late 2025, 57.3 percent of organizations had agents in production, up from 51 percent a year earlier, according to LangChain's survey of more than 1,300 professionals. Three failure modes dominate the postmortems: degenerative reasoning loops, hallucinated data in RAG systems, and silent misalignment between the request and the interpretation.
SQLite in production is a sound choice for small and mid-sized web applications once WAL mode is enabled, since concurrency on typical web loads improves by roughly two orders of magnitude. Litestream streams the WAL to S3-compatible object storage for point-in-time restore, and NVMe disks on cheap VPS plans removed the old disk objection.
Tras dos años de RAG en producción, patrones claros emergen: chunking inteligente, hybrid search, re-ranking, evaluación continua. Qué evitar.
