agent-quality

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

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

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.

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

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

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

Deterministic spec-driven development CLI

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