This article shows how AgentLoop creates a backtesting plan, uses an offline intranet experiment platform, schedules regular runs, visualizes results on an experiment dashboard, and applies per‑question Rubrics to reliably measure and iterate on AI agent improvements.
This article presents a complete, data‑driven workflow for creating, instrumenting, offline evaluating, analyzing bad cases, and iteratively optimizing Skills on Alibaba Cloud AgentLoop, enabling developers to quantify quality, track improvements across versions, and reliably deliver high‑quality AI Agent capabilities.
Evaluating AI agents requires more than accuracy; you must measure task completion, execution trace, tool usage, latency, cost, error rates, and both explicit and implicit user feedback, using observability, offline smoke‑test and regression suites, and continuous online monitoring to create a closed‑loop improvement process.
The article reveals that many AI product managers can talk about AIGC and agents but stumble when asked to design a rigorous evaluation system, illustrating the problem with a chatbot case study and presenting a detailed 1+3 multi‑dimensional framework to guide product definition, development, and iteration.
This article explains what data metrics are, compares offline metric testing with traditional testing, and provides a comprehensive step‑by‑step guide for testing data collection, ETL, warehouse models, metric calculations, scheduling, security, and API outputs in a Hive‑based data warehouse.
This article explains the background, architecture, challenges, and step‑by‑step design of a big‑data‑driven risk control experiment platform used for online and offline strategy testing in internet finance.
The article explains why and how to store external libraries and tools in a private artifact repository, isolate builds from the internet, and verify build stability by testing offline pipelines, emphasizing the importance of managing dependencies for reliable DevOps processes.
This article explains the evolution, responsibilities, and evaluation methods of the product‑effect assessment team at Amap, covering offline testing, AB experiments, metric analysis, automated scoring models, and the tools that support a comprehensive product‑performance framework.
The article explains the core concepts of recommendation engines—user profiling and data classification—describes how large‑scale data processing tools are used to build models, and outlines common offline and A/B testing approaches for evaluating recommendation performance.
