Production LLM Reliability

Production LLM reliability is the engineering discipline of making model-based systems bounded, observable, recoverable, evaluable, and verifiable enough for real users and high-stakes workflows. The bayer-prince case argues that production-ready agentic AI is not only better prompts or models; it requires explicit control over context, workflow state, recovery, reflection, citations, and human review.^{source: martin-fowler-bayer-reliable-agentic-ai-systems-2026.md}

PRINCE's reliability pattern has several layers: persist state so failed workflows resume from the failed LangGraph node, retry transient failures at both LLM-call and workflow-node levels, fall back across LLM providers, expose intermediate steps and selected context to users, attach sentence-level citations to source documents and page numbers, trace production traffic in Langfuse, and run both curated dataset evaluations and daily live-traffic evaluations.^{source: martin-fowler-bayer-reliable-agentic-ai-systems-2026.md}

The broader lesson connects directly to harness-engineering: reliability comes from engineering both the context the model sees and the harness within which it acts. In regulated research environments, explicit harness control remains valuable even as models improve, because trust depends on traceability, reviewability, and recovery paths rather than raw generation quality alone.^{source: martin-fowler-bayer-reliable-agentic-ai-systems-2026.md}

Ronacher's loop critique adds a reliability boundary for agentic production systems: a system can become operationally useful while becoming less human-comprehensible. Reliability work therefore has to measure more than task completion; it should preserve traceability, legible change history, invariant enforcement, and human supervision so the organization does not become dependent on machines merely to understand the machines' previous work.^{source: armin-ronacher-the-coming-loop-2026.md}

Production LLM Reliability

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