The open-source MultiAgentOps evaluation and verification harness for any industry business workflow.

Detecting Relational Boundary Erosion in AI systems. A framework for testing whether models maintain honest, calibrated, and appropriate boundaries.

VLA ≠ VLM. Side-by-side viewer running NVIDIA Alpamayo R1 (vision-language-action) alongside Qwen2.5-VL (vision-language) on the same 44-sec SF dashcam clip at 5 Hz. 220 paired traces. Surfaces what an action-trained model sees that a scene-trained model doesn't, and vice versa.

Wellness verification harness for companion AI. Multi-turn adversarial suites grounded in six decades of mental-health research and current clinical standards (988, VERA-MH) and law (SB 243). Point it at any chat endpoint — get an evidence-backed, reproducible report.

Constitutional governance platform for multi-agent AI systems — 261 personas, 17 divisions, a judiciary, RBAC, and a written constitution

AI content engine using an anxiety-indexed behavioral science KB, multi-stage LangGraph pipeline, and calibrated LLM-as-judge evaluation harness

Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild

An LLM-powered training-evaluation platform that scores open-ended scenario responses 0 to 10 against rubrics, with an evaluation harness that benchmarks the AI scorer against human-labelled scores.

Enterprise RAG lab using AWS Bedrock, Snowflake, MuleSoft, Python, and an evaluation harness for regulated lending scenarios.

A benign local test suite for evaluating instruction-authority boundaries in AI browsers and browser agents.

DoE Project

Prompt-evaluation toolkit: run golden-case prompts, route models, track cost, and leaderboard.

LLM evaluation harness that archives every eval run — input, output, score, and trace — as immutable JSON on Backblaze B2. Run prompt evals against multiple Claude models, score with built-in + LLM-as-a-judge scorers, and diff runs to catch regressions.

An end-to-end framework for running, sandboxing, and scoring agentic LLMs on complex data-science and econometric replication tasks.

Agentic AI proof of concept for last-mile delivery exception handling using n8n, RAG, tool-calling agents, critic validation, and evaluation metrics.

Marketing claims-review agent that cites support, flags unsupported copy, and gates published-falsehood rate in CI.

Controlled experiment isolating reranking as a first-class RAG system boundary, measuring how evidence priority—not recall—changes retrieval outcomes.

Evaluation harness for domain-specific RAG and QA systems with benchmark datasets, scoring, and regression workflows.

An open-source CLI harness for measuring AI answer-evaluator reliability — noise floor, invariance rate, regression detection precision.

AI-agent evaluation harness with rubrics, regression datasets, deterministic checks, and reports.