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.
Computer-use agents turn model decisions into clicks, typing, uploads, and transactions. Evaluate permissions, sensitive data, reversibility, recovery, and honest handoff before deployment.
Read moreAgent failures cross model, retrieval, tool, orchestration, and runtime boundaries. Use first-divergence analysis to assign the failure to the layer that can actually fix it.
Read moreWhy AI agent evaluation still needs humans in 2026, where to put them in the loop, and how to combine human review with automated evals on production traces.
Read moreA practical field guide to LLM evaluation tools — what each category is good at, where they break down, and how to pick one that survives contact with production traffic.
Read moreThe best AI observability tools in 2026 compared on evaluation depth, quality-aware alerting, drift detection, cost tracking, and the production-to-eval loop.
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