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.
Not every eval needs a model to grade it. Here's how to decide between deterministic metrics you can trust blindly and LLM-as-a-judge scoring you have to calibrate — and why the best suites use both.
Read moreWhat a customer service chatbot actually is in 2026, how modern AI chatbots work, the main types, and how to choose one that improves support quality.
Read moreGuardrails are the runtime checks that sit between your model and the world — catching leakage, injection, and unsafe output in real time. Here's what to guard, where guards go, and why guardrails and evals need each other.
Read moreA practical end-to-end playbook for LLM evaluation in 2026 — from defining quality and building datasets to choosing metrics, running evals, and closing the loop.
Read moreCompare the best Crisp alternatives for AI customer support in 2026 on AI answers, channels, integrations, pricing, and how they handle knowledge and escalation.
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