ProofAgent is the accountability platform for production AI agents. It turns agent risk into deployment evidence through adversarial multi-juror scoring, production log audits, artifact reviews, signed readiness reports, and human review. The platform is built around the open-source ProofAgent Harness.

How do I test my AI agent with ProofAgent?

Install the open-source harness with 'pip install proofagent-harness', wrap your agent in a function returning AgentResponse, then call Harness().evaluate(my_agent, role, goal, knowledge, context). The harness runs adversarial multi-turn sessions and returns a /10 readiness score with traceable findings and fix recommendations.

What is adversarial multi-juror scoring?

Adversarial multi-juror scoring is ProofAgent's evaluation approach: a planner picks domain traps, a conductor applies sustained pressure across 25+ turns, and three independent juror agents score every behavior change. No single LLM call ever decides the verdict — the jury agents reach consensus or debate to a final score.

Is ProofAgent SOC 2 / HIPAA / GDPR compliant?

ProofAgent is SOC 2 Type II aligned, HIPAA-ready (BAAs available for enterprise customers), and follows GDPR best practices. Enterprise customers can deploy on-premises or in a private cloud with SSO/SAML, RBAC, tamper-evident audit logs, TLS 1.2+ in transit, and AES-256 at rest.

Can I use my own LLM with ProofAgent?

Yes. ProofAgent is BYO Harness LLM — the harness internals can run on any LLM provider (OpenAI, Anthropic, Google, local models). You bring your own model and API key; the harness orchestrates the multi-juror evaluation around it.

What metrics does ProofAgent measure?

11+ production metrics including Task Success, Hallucination Control, Safety, Policy Compliance, Memory Stability, Tone and Empathy, Manipulation Resistance, Tool Picking, Reasoning Quality, Relevance, and Drift Detection. Every metric is anchored to per-turn transcript evidence.

What is the difference between ProofAgent Platform and ProofAgent Harness OSS?

ProofAgent Harness OSS is the open-source multi-turn adversarial testing engine — Tier 1 of the platform, available standalone for developers and CI under Apache 2.0. ProofAgent Platform is the enterprise product that adds the other four tiers (production log audit, artifact review, multi-agent orchestration scoring, expert human review), a hosted dashboard, REST API, governance features, signed readiness reports, and dedicated support.

Human-on-the-Bridge: Scalable Evaluation for AI Agents

Name: ProofAgent Platform
Brand: ProofAgent
Availability: InStock

Human-on-the-Bridge (arXiv:2606.16871) by Dr. Fouad Bousetouane is the research paradigm behind the open-source ProofAgent Harness. Instead of putting a human reviewer in the loop on every agent run, it curates human expertise once — upstream — into reusable traps, juror personas, and scoring rubrics, then lets small evaluator models stress-test frontier-grade AI agents at scale.

Not a human in the loop — a human on the bridge

A captain on the bridge sets the course, the rules, and the instruments rather than steering every wave. Human-on-the-Bridge applies the same separation to AI agent evaluation: experts invest judgment once into the evaluation machinery, and that machinery then runs automatically on every agent and every release — rigorous because it carries real expertise, scalable because it runs locally and cheaply without a reviewer babysitting each transcript.

What the paper contributes

Human expertise curated upstream into reusable evaluation intelligence: traps, juror personas, and scoring rubrics
Small evaluator models reliably assess agents built on frontier LLMs when armed with pre-curated expert judgment
Multi-turn adversarial pressure surfaces policy drift, manipulation, and tool-call failures that static benchmarks miss
Multi-juror consensus through debate or Delphi reduces single-model judging bias
Every mechanism is reproducible and open-source under Apache 2.0 in the ProofAgent Harness

Headline finding

Production-grade agents built on frontier LLMs (GPT 5.5, Claude Opus 4.7) fail under sustained, composite adversarial pressure — and a small, local evaluator model armed with pre-curated expert judgment can reliably catch it. Frontier models alone are not enough; the agent layer needs its own evaluation infrastructure, and that infrastructure does not have to be as large as the agent it tests. The companion core whitepaper (arXiv:2605.24134) documents the full pipeline and experimental results.

Plain language explainer on Medium: Human-on-the-Bridge (HoB): The New Paradigm for Scaling AI Agent Evaluation.