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.
Trace agent memory reads and writes, then evaluate retrieval, freshness, contradiction handling, privacy, usefulness, latency, and cost.
Read moreTrace deep agents across planning, subagents, files, tools, and long-running tasks, then evaluate outcomes, trajectories, recovery, cost, and latency.
Read moreImprove an AI agent by evaluating its harness: prompts, tools, context assembly, retries, stop conditions, and orchestration—not only its model.
Read moreAgents cannot use tools they fail to discover. Evaluate retrieval, ranking, schema understanding, and execution separately to find the real limit on tool-calling quality.
Read moreAn agent-generated result is not productive if a human must spend an hour repairing it. Add review, correction, rerun, and cleanup effort to your evaluation scorecard.
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