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OpenAI's top AI Agent was stress-tested in real business scenarios to see if it could independently run a company. The experiment reveals agent capabilities and limitations in decision-making, memory, and strategic planning.

Deep analysis of how Cekura's five-step closed loop—scenario simulation, failure capture, root cause diagnosis, automatic prompt rewriting, and regression verification—solves voice AI agent quality assurance in production.

API Mock is fast but misses bugs; Sandbox is realistic but costly. This article analyzes their core differences and provides a layered testing strategy for building reliable Agent test systems.

An in-depth look at AI testing challenges. Learn to write reusable Skill packs and master Agent testing and LLM evaluation—covering the SKILL.md six-dimensional rule, skill-creator, EvalScope, and dataset selection.

What is the fundamental difference between terminal agents and device agents? This article breaks down Claude Code's core positioning, the key logic for enterprise AI testing selection, and the advantages of the Claude Code + DeepSeek combination.

An in-depth look at how AI Agents disrupt traditional software testing: the core differences between LLMs and Agents, four capability dimensions (planning/memory/tools/skills), and how testers achieve 10x efficiency gains.

An in-depth look at how AI Agents are disrupting traditional software testing: the core differences between LLMs and Agents, four capability dimensions (planning/memory/tools/skills), and how test engineers can achieve 10x efficiency gains in test case generation.

Full breakdown of a real AI testing pipeline: API collection, doc enrichment, AI test case generation, Agent-driven execution, and test reports — with Skills, RAG, and Harness engineering.

Veta is an open source AI testing agent: just describe your test goal in natural language and it autonomously plans, executes, verifies, and reports Android test results — no scripts needed.

A deep dive into AI testing workflows: API capture, test case generation, Agent orchestration, and automated execution. Learn the two core challenges — incomplete information and mandatory human review.

Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.

QuantaMind is a free, open-source local AI Agent reliability testing tool using pass^k scoring and deterministic evaluation, supporting Ollama, llama.cpp, vLLM, and more.

A deep dive into AI-powered testing: Cursor Skills, Coze agents, and LangChain multi-agent systems for automated test case generation, BDD, and review workflows.

Deep dive into the Cocos Creator AI Agent Plugin: three-level context compression, tool approval, multi-agent parallelism, and DeepSeek-powered Mario scene building — all in one test.

AI Job Search is an open source, Claude Code-powered job search automation tool with over 20K GitHub stars. It offers resume tailoring, cover letter generation, dual-Agent QA, and keeps humans in control of submission.

Learn Claude Code from scratch: understand LLMs vs. AI agents, explore a 3-day onboarding path, and discover how testing engineers can use agents to automate test case and script generation.

Learn how to build an AI-driven API automation testing framework using Agent+Skill architecture with Claude Code, covering test case generation, script execution, and report output.
TutorialsSupabase's experiments show how MCP+Skills solve security gaps when AI agents operate databases, with three key principles for writing effective Agent Skills.
TutorialsHands-on comparison of Claude Code, Codex, and DeepSeek TUI for AI-assisted penetration testing with DeepSeek V4 Pro, covering vulnerability discovery, WebShell upload, and intranet penetration.
TutorialsDon't trust AI's self-reported test results. Learn three-layer acceptance criteria using HTTP status, business logs, and database persistence to verify AI Agent outputs.