Blog
Highlights from the Currai blog: the posts worth reading first.
A complete, code-first guide to turning OpenAI Realtime voice conversations and text-to-speech generations into readable Currai User Stories and correctly nested traces.
A step-by-step guide to importing completed Retell AI calls into Currai with signed call-ended webhooks—without adding capture code to the live voice agent.
A step-by-step guide to importing completed Vapi calls into Currai with an authenticated webhook—without adding capture code or sharing a Vapi private API key.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
Agentic 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.
Read moreLearn how to mine production traces for recurring agent failures, turn the patterns into evals, and close the AI agent improvement loop with Currai.
Read moreAI observability should do more than store traces. Currai turns traces into active signals for quality, cost, latency, prompts, tools, and evals.
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