Aprenda avaliar a qualidade do seu agente de AI, RAG e LLM
agents rag
| Source: Dev.to | Original article
A tutorial and accompanying blog post released on 19 April 2025 by Brazilian AI practitioner Airton Lira Jr. offers the first end‑to‑end playbook for measuring the performance of autonomous AI agents, retrieval‑augmented generation (RAG) pipelines and the underlying large language models (LLMs). The guide, titled “Aprenda avaliar a qualidade do seu agente de AI, RAG e LLM”, bundles a step‑by‑step notebook that builds a RAG application with the Mosaic AI Agent Framework, runs the new “Agent Evaluation” suite, and translates raw scores into actionable insights.
The timing is significant. Over the past year, Nordic developers have been racing to ship locally‑run agents—Lore 0.2.0, the SQLite‑backed “localmind” CLI, and other eval‑driven tools—yet a common yardstick for quality has remained elusive. Lira’s work aggregates the metrics championed by IBM and recent academic surveys: task success rate, hallucination frequency, latency, token‑efficiency, and cost per inference. By automating these checks within a reproducible notebook, the guide lowers the barrier for continuous evaluation, a practice we highlighted in our 19 April 2026 report on shipping Lore 0.2.0 with confidence.
Practitioners can now embed the evaluation pipeline into CI/CD, catch regressions before deployment, and produce audit‑ready reports that align with emerging EU AI‑Act requirements. The broader AI community is already citing the tutorial as a reference point for benchmark creation, and Mosaic has announced a forthcoming integration with the Implicator LLM Meter, which recently saw Gemini overtake ChatGPT on that scale.
What to watch next: adoption of Lira’s framework by open‑source projects such as localmind, the rollout of standardized agent benchmarks by European consortia, and potential updates from IBM on enterprise‑grade evaluation tooling. If the guide gains traction, it could become the de‑facto baseline for trustworthy agent development across the Nordic AI ecosystem.
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