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.
A comprehensive 2026 guide to evaluating AI agents — scoring reasoning, tool use, actions, and outcomes across multi-step runs, not just final answers.
Read moreHow to use LLMs to generate evaluation and training data that's actually useful — seeding a test set before you have traffic, covering edge cases you'd never hit organically, and avoiding the traps that make synthetic data lie to you.
Read moreHow to evaluate tools exposed through the Model Context Protocol — testing tool selection, argument correctness, execution, and task outcomes step by step.
Read moreUse the Model Context Protocol to manage your website chatbot from Claude — update knowledge, review conversations, and onboard the agent conversationally.
Read moreHow comparison-based LLM evaluation (arena-as-a-judge) works — pairwise judging of two outputs, why it beats absolute scoring for subtle quality, and how to use it.
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