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.
What the popular LLM benchmarks actually measure, why a high leaderboard score doesn't mean your app will be good, and how to read benchmarks as a starting point instead of an answer.
Read moreAI agent evals need more than a final answer score. Learn how to evaluate tool calls, transcripts, outcomes, regressions, non-determinism, and production traces.
Read moreThe best AI eval tools for CI/CD catch prompt, model, retrieval, and agent regressions before deploy. Compare Currai, Promptfoo, Braintrust, Langfuse, and Phoenix.
Read moreA practical 2026 guide to AI chatbot compliance: disclosure rules, data protection, sector regulations, and the controls that reduce legal risk.
Read moreTesting an LLM app isn't like testing normal software — outputs are non-deterministic and open-ended. Here are the testing methods that actually work: unit, regression, adversarial, and production, and how they fit together.
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