Blog
Practical posts on tracing, evals, prompt changes, token cost, and the production habits that keep AI products explainable.
Highlights from the Currai blog: the posts worth reading first.
Why AI agent evaluation still needs humans in 2026, where to put them in the loop, and how to combine human review with automated evals on production traces.
A practical field guide to LLM evaluation tools — what each category is good at, where they break down, and how to pick one that survives contact with production traffic.
The best AI observability tools in 2026 compared on evaluation depth, quality-aware alerting, drift detection, cost tracking, and the production-to-eval loop.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
Guardrails are the runtime checks that sit between your model and the world — catching leakage, injection, and unsafe output in real time. Here's what to guard, where guards go, and why guardrails and evals need each other.
Read moreA practical end-to-end playbook for LLM evaluation in 2026 — from defining quality and building datasets to choosing metrics, running evals, and closing the loop.
Read moreAgentic 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 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.
Read moreCompare the best Crisp alternatives for AI customer support in 2026 on AI answers, channels, integrations, pricing, and how they handle knowledge and escalation.
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