4 articles tagged Llmobservability
Prompts are code: how to measure their value
Prompts and agent graphs need outcome metrics, not token counts. A practical guide to boundary instrumentation, versioning, and avoiding vanity metrics.
19 min read · 16 October 2026
Multi-agent systems: when they quietly degrade
Multi-agent LLM systems drift into slow, costly behaviour without failing loudly. Here is what to instrument, what to budget, and how to catch the slide.
12 min read · 24 September 2026
Observability for LLM Features: Metrics That Drive Decisions
Log one structured event per LLM interaction instead of tracing every token, then alert only on user-facing outcomes that justify keeping a feature.
14 min read · 8 July 2026
LLM drift: the silent quality drop metrics miss
Latency and error rate stay flat while LLM output quality quietly degrades. Here is how to log prompts, measure embedding drift, and catch it early.
11 min read · 4 July 2026