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.
Currai helps teams move from random trace inspection to continuous trace intelligence: finding repeated AI failures and turning them into evals.
Read moreAI observability should do more than store traces. Currai turns traces into active signals for quality, cost, latency, prompts, tools, and evals.
Read moreHow AI chatbot pricing really works in 2026 — per seat, per message, per resolution, and usage models — and how to model your true cost before buying.
Read moreStateful agent evals need more than a final answer score. Currai ties agent steps, tool calls, sessions, cost, latency, and eval results back to the production trace.
Read moreAI evals are not just engineering tests. Product managers need real traces, domain judgment, and a repeatable loop for turning model failures into product improvements.
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