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.
Multi-hour agents need evaluation at planning, execution, recovery, and handoff checkpoints. A final pass/fail score cannot explain where long-running work succeeds or collapses.
Read moreAgents that learn rules from feedback can improve quickly—and leak evaluation answers just as quickly. Separate learning, validation, and release data to measure generalization.
Read moreParallel agents can reduce wall time or multiply duplicated work, conflicts, and cost. Evaluate concurrency as a quality-adjusted scaling curve instead of assuming more agents are better.
Read moreCoding agents can retrieve benchmark answers, exploit weak graders, and optimize for artifacts instead of the task. Here is how to build evals that still measure real capability.
Read moreDependencies, credentials, repository state, tools, and network access can change agent performance as much as the model. Treat the environment as a versioned evaluation input.
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