Enterprise audit agent workspace with Agentic RAG, governed tool use, evaluation harness, memory, and human-review delivery workflows.

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

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

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.

Does a CLAUDE.md actually change how Claude behaves? An ablation harness: run adversarial traps with the rules and without them, grade blind, and test whether the difference is real.

Autonomous financial research agent combining live market data, financial news, sentiment analysis, and private RAG with transparent execution.

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

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

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

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.

AIの成果が「本物か、まぐれか」を統計で切り分ける4層検証パイプライン（取得→防御→検証→運用）。LLM評価・実ログ障害トリアージに実適用済み

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

Chaos engineering for multi-agent LLM meshes — which topology survives which failure? Runs on Nebius Serverless: CPU Job sweeps against a vLLM Endpoint, per-trial Object Storage checkpoints, kill-and-recover by design.

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

DoE Project

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.

Offline classifier evaluation harness — dataset loader, confusion matrices, LLM-as-judge with cost accounting, regression gates for CI, Phoenix/Langfuse exporters. Built for intent classifiers but works on any classification task.