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.

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