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.
How to use LLMs to generate evaluation and training data that's actually useful — seeding a test set before you have traffic, covering edge cases you'd never hit organically, and avoiding the traps that make synthetic data lie to you.
Read moreHow to evaluate tools exposed through the Model Context Protocol — testing tool selection, argument correctness, execution, and task outcomes step by step.
Read moreUse the Model Context Protocol to manage your website chatbot from Claude — update knowledge, review conversations, and onboard the agent conversationally.
Read moreBuild a recruitment chatbot that screens candidates, answers job questions, schedules interviews, and syncs to your ATS — fairly and around the clock.
Read moreThe parts every LLM eval framework needs — datasets, metrics, a runner, and a results store — and an honest look at when to build your own versus when a homegrown harness quietly becomes the thing you maintain instead of your product.
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