As enterprises push large language models into production, the author argues reliability and governance cannot rest on wishful thinking. Without observability, Artificial Intelligence decisions are opaque, untraceable and ungovernable. A cited Fortune 100 bank case shows the risk: benchmark metrics looked good, but 6 months later auditors found that 18% of critical cases were misrouted with no alert or trace. The piece frames observability as the foundation of trust, asserting that if you cannot observe a system you cannot trust it.
Practical guidance begins by flipping the typical order: define outcomes first, not models. Example outcomes include Deflect 15 % of billing calls, Reduce document review time by 60 % and Cut case-handling time by two minutes. Telemetry should be designed around those business goals. The article proposes a 3-layer telemetry model for LLM observability: a) prompts and context, logging prompt templates, model ID, version, latency and token counts and an auditable redaction log; b) policies and controls capturing safety-filter outcomes, citation presence, policy reasons and links to model cards; and c) outcomes and feedback tracking human ratings, downstream business events and KPI deltas. All layers connect via a common trace ID to enable replay, audit and improvement.
Service reliability engineering practices are applied to reasoning with SLOs and error budgets. Three golden signals are recommended: Factuality with a target of ≥ 95 % verified against source of record, Safety with a target of ≥ 99.9 % pass toxicity/PII filters and Usefulness with a target of ≥ 80 % accepted on first pass. Breaches trigger fallbacks such as verified templates, quarantine and human review or retrain/rollback. The author prescribes two agile sprints to build a thin observability layer (Sprint 1 weeks 1-3, Sprint 2 weeks 4-6), offline test sets of 100-300 real examples, continuous evaluations with 10-20 % monthly refresh and human-in-the-loop escalation for high-risk cases. Within 3 months organizations should have 1-2 production Artificial Intelligence assists with HITL for edge cases, an automated evaluation suite, weekly scorecards and audit-ready traces. The article concludes that observability is not an add-on but the foundation for trust at scale and cites business wins such as a 40 % reduction in incident time and a 22 % drop in false positives at client deployments.
