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.
AI evals work best when product, support, engineering, and domain experts share traces, rubrics, and quality decisions in Currai.
Read moreHow AI chatbot pricing really works in 2026 — per seat, per message, per resolution, and usage models — and how to model your true cost before buying.
Read moreStateful agent evals need more than a final answer score. Currai ties agent steps, tool calls, sessions, cost, latency, and eval results back to the production trace.
Read moreAI evals are not just engineering tests. Product managers need real traces, domain judgment, and a repeatable loop for turning model failures into product improvements.
Read moreA plain-English guide for small business owners choosing an AI chatbot in 2026: what it can do, what to look for, what it costs, and how to start small.
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