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.
Evaluate multi-agent RAG systems for financial data by separating retrieval, routing, calculations, citations, synthesis, and policy compliance.
Read moreUse this practical checklist to confirm your agent has measurable goals, representative datasets, reliable traces, calibrated evaluators, and release gates.
Read moreDesign reusable LLM evaluators with stable rubrics, typed inputs, calibration examples, and versioning that work in development and production.
Read moreA risk-weighted evaluation framework for deciding which agent actions can run automatically, which need review, and which should remain blocked.
Read moreContext trimming, tool edits, retry policies, and model-specific tuning can degrade agents without breaking tests. Use paired trajectory evals to catch silent harness regressions.
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