This guide presents a comprehensive quality assurance framework for AI agent systems, covering tool call correctness, boundary behavior control, trajectory-level assertions, regression testing, chaos engineering, and CI/CD integration using tools like AgentVal, dryfire, tooltangle, and probe-ai.
MiniAppBench, the first benchmark that evaluates large language models' ability to generate fully functional interactive HTML applications, shows an average pass rate of just 17% across 16 top models—with the strongest model, GPT‑5.2, achieving only 45%—highlighting a substantial gap between current capabilities and real‑world user requirements.
The article reviews five production‑grade open‑source frameworks—LangTest, AgentScope, VerifyMe, AgnosticTest, and TestLLM—detailing their design philosophies, core capabilities, suitable scenarios, and real‑world case studies to help testing professionals evaluate reliability, controllability, explainability, and evolvability of LLM agents.
The article outlines how software testing is shifting from functional correctness to trustworthy behavior verification for AI agents in 2026, detailing a three‑dimensional trust matrix, agent‑native CI pipelines, human‑AI collaborative testing, and compliance‑driven auditable agents with concrete industry examples and metrics.
This article shares five surprising, high‑impact lessons from Anthropic on building robust AI agent evaluation suites, covering early failure‑case collections, recognizing clever “failures,” focusing on outcomes over process, choosing the right success metrics, and the irreplaceable value of human review.
Anthropic’s new blog reveals a comprehensive framework for evaluating AI agents, detailing evaluation structures, metrics like pass@k and pass^k, types of scorers, multi‑round testing, and a step‑by‑step roadmap for designing, maintaining, and integrating automated assessments into agent development pipelines.
This guide explains how to systematically evaluate industry‑specific AI agents by testing the combined model and engineering stack, building domain‑expert‑driven datasets, designing reproducible testing systems, managing assets, controlling costs, and applying both traditional and LLM‑based methods to ensure reliable, stable performance.
The article explains how Coze's Compass introduces a flexible evaluation system for AI agents, outlines a four‑dimensional submodule assessment (planning, tool use, self‑reflection, memory), and details specific testing criteria and challenges for web, scientific, dialogue, and programming agents.
