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.
Even a well-evaluated AI system fails in production through distribution shift, integration and compounding errors, and unmeasured dimensions. Here's how to catch each.
Read moreG-Eval turns a plain-language quality criterion into a repeatable score by having the judge reason through steps first. Here's how it works, why it beats a bare 1–5 prompt, and where it still needs calibration.
Read moreAgentic customer service means AI that resolves issues by taking actions, not just answering. Here's how it works, where it helps, and how to deploy it safely.
Read moreA practical architecture for connecting production traces, evaluation datasets, offline experiments, and online evals into one agent improvement loop.
Read moreDiagnose rising coding-agent costs with per-step traces, then reduce LLM spend without hiding quality regressions or task failures.
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