Lighthouse for agents - score an agent run, then tell it how to get faster and cheaper.

Benchmarking the ability of large language models to detect semantic conflicts across domains, documents, and evolving knowledge bases.

AI Agent Root Cause Analysis Protocol — A systematic five-question methodology adapted from Toyota's 5 Whys for diagnosing failures in AI agent systems. Battle-tested over 60+ production incidents.

Pre-Execution Gate for AI Code. A deterministic, gradient-immune structural guard against reward hacking and hardcoding in RL training loops.

Professional cross-agent answer quality gate for improving AI responses: intent match, evidence, assumptions, verification, brevity, and usefulness.

LLM Agent quality metrics — structured recording and quality threshold testing for Function Calling agents

Field-tested QA validation gates for AI agent systems. Tiered gates, protocol gates, severity classification, and automated checks. Born from production failures.

Deterministic spec-driven development CLI