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.
Learn 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.
Read moreActive observability turns production LLM traces into continuous signals for quality, cost, latency, prompts, tools, and evals before users report a problem.
Read moreAn agent can call the right tool and still fail the task — or complete the task despite a clumsy path. Here's how to score tool-calling and task completion as separate metrics, so a failure points to a fix.
Read moreTrace, span, generation — three nouns that cover everything an LLM app does. Understand the data model and your instrumentation stops being guesswork.
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