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 startups should approach LLM evaluation in 2026 — the minimum viable eval setup, what to measure, and how to build a quality loop without a big team.
Read moreChatbots fail across a conversation, not in a single reply. Here are the metrics that actually catch chatbot failures — and why turn-level scoring alone always misses them.
Read moreA practical review of Intercom's Fin AI agent in 2026: how it works, its resolution-based pricing, where it fits, and when to consider alternatives.
Read moreUse the ChatGPT API as an LLM judge with stable rubrics, structured scores, bias controls, human calibration, and production trace evaluation.
Read moreThe OWASP Top 10 for LLM applications, translated from a checklist into detectable behaviors — what each risk looks like in a real trace, and how to catch it before it becomes an incident.
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