Blog
Highlights from the Currai blog: the posts worth reading first.
Use real production conversations to find agent failures, make focused prompt changes, build better evals, and verify that user outcomes improve.
Monitor customer success agents with end-to-end traces, conversation outcomes, groundedness, escalation quality, policy compliance, latency, and cost.
A beginner-friendly, code-first guide to turning Vapi browser calls into Currai sessions, conversation User Stories, and correctly nested voice-agent traces.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
How to evaluate multi-turn LLM conversations in 2026 — measuring context retention, goal completion, and turn-level quality that single-response evals miss.
Read moreWhy public benchmarks tell you almost nothing about your app, which metrics actually predict production quality, and the evaluation practices that separate teams who ship confidently from teams who guess.
Read moreA comprehensive 2026 guide to evaluating AI agents — scoring reasoning, tool use, actions, and outcomes across multi-step runs, not just final answers.
Read moreHow comparison-based LLM evaluation (arena-as-a-judge) works — pairwise judging of two outputs, why it beats absolute scoring for subtle quality, and how to use it.
Read moreThe core RAG evaluation metrics explained — answer relevancy, faithfulness, contextual precision, contextual recall, and contextual relevancy — and what each catches.
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