Mathematical benchmark exposing the massive performance gap between real agents and LLM wrappers. Rigorous multi-dimensional evaluation with statistical validation (95% CI, Cohen's h) and reproducible methodology. Separates architectural theater from real systems through stress testing, network resilience, and failure analysis.

Proof your AI agent still works. Regression testing with golden baselines, tool-call diffing, and output drift detection. MCP server + Claude Code skills. LangGraph, CrewAI, Anthropic, OpenAI.

TraitBasis applied to TauBench

A curated collection of the world’s most advanced benchmark datasets for evaluating Large Language Model (LLM) Agents.

The CI reliability gate for action-oriented agents.

🧠 Discover and evaluate advanced benchmark datasets for Large Language Model agents to enhance performance assessment in real-world tasks.

University for AI agents. 92 courses, 4400+ scenarios, any model via OpenRouter. Auto-training loops generate per-model SKILL.md documents. Works with Claude Code, OpenClaw, Cursor, Windsurf. No fine-tuning required.

AI Arena is a competitive evaluation framework where multiple AI agents answer the same set of questions under identical conditions. Their performance is scored, ranked, and tracked over time using two complementary metrics AIQ and ELO

🤖 Benchmark AI agent capabilities, bridging the gap between hype and reality with clear metrics and insights for informed development decisions.