97.5% failure rate on real work — Scale AI Remote Labor Index

Your Agent Passes Benchmarks.
Can It Pass Reality?

OpenAI's benchmarks show agents completing tasks 100x faster than humans. Scale AI's real-world tests show a 97.5% failure rate. The difference? Context. We evaluate whether your agent — and your organization — has the context to deploy safely.

The Problem

  • - 97.5% of agents fail on real freelance work (Scale AI)
  • - 75% break working code during maintenance (Alibaba SWE-CI)
  • - 55% of companies regret AI-driven layoffs (Forrester)
  • - Enterprises can't reliably assess AI vendor security (AIUC-1 Consortium)
  • - No standard way to verify if an agent is trustworthy before transacting

What We Do

  • + Evaluate agent AND organization (not just the agent)
  • + 126 checks from NIST AI RMF, CSA, and eIDAS 2.0
  • + 3-model consensus scoring — no single-model bias
  • + AIUC-1 aligned framework
  • + Verifiable credential (W3C standard via cheqd)
  • + Continuous monitoring keeps the trust score honest

We Don't Just Test What Your Agent Can Do

We evaluate whether your organization has the infrastructure for safe agent deployment.

WHO is accountable?

Named owner, legal entity, kill switch, escalation path

Ownership

CAN it prove identity?

Unique ID, agent card, scoped credentials, auth schemes

Identity

DOES it do what it claims?

Task completion, tool usage, grounding, boundary adherence

Task

WHAT if someone breaks it?

Prompt injection, PII protection, harmful content, privilege escalation

Safety

ARE there controls?

Logging, incident response, risk policies, audit trails, insurance

Governance

WILL it stay up?

Uptime, TLS, error handling, anomaly detection, load resilience

Runtime

Even a Perfect Agent Fails in a Broken Environment

We also assess your agent's operating environment against 8 readiness pillars. An L3 agent in an L1 environment will still fail.

Style & Validation
Build System
Testing
Documentation
Dev Environment
Code Quality
Observability
Security & Governance

Based on the Agent Readiness Framework. Target: Level 3+ before deploying autonomous agents.

Three Evaluation Tiers

L-A

Assessed

From $499

Agents behind firewalls or without A2A endpoints

  • + Evidence + governance review (no live testing)
  • + 126-check scoring across 6 domains
  • + 3-model consensus (Claude + GPT-4o + Gemini)
  • + Environment readiness assessment
  • + Trust badge + verification page
  • + 8-document deliverable package
  • - Live behavioral testing
  • - Continuous monitoring
Most Popular
L-T

Tested

From $2,500

Agents with A2A endpoints or public APIs

  • + Everything in Assessed, plus:
  • + Live adversarial testing (9+ scenarios)
  • + Prompt injection, boundary, PII, scope testing
  • + A2A protocol compliance verification
  • + Tool trajectory analysis
  • + W3C Verifiable Credential (L2+)
  • + Environment readiness recommendations
  • - Continuous monitoring
L-V

Verified

From $5,000/yr

Production agents handling transactions or sensitive data

  • + Everything in Tested, plus:
  • + Continuous monitoring (credit score for agents)
  • + Anomaly detection with auto-suspension
  • + Quarterly re-testing (AIUC-1 requirement)
  • + Drift alerts (minor → downgrade → suspend → revoke)
  • + Agent history timeline + score trends
  • + Credential renewal management

How It Works

Week 1-2

Scope & Verify

Define scope, verify owner identity (KYC), set scoring profile

Week 2-5

Evaluate

Evidence collection, adversarial testing, 3-model consensus scoring

Week 5-7

Review & Credential

Expert review, trust level assigned, W3C credential issued (L2+)

Ongoing

Monitor

Continuous scoring, drift alerts, quarterly re-testing, renewal

“A power tool that fails silently is very dangerous. The best tools for managing that danger are human brains and human judgment about what matters, paired with evaluations to encode that judgment.”

— Industry Research, 2026

Your Agents Pass Benchmarks.
Let's See If They Pass Reality.

First evaluation takes 4-8 weeks. Start now.

Request Evaluation →