Stop fixing your client's
AI agent at 3 AM.

A user found a prompt injection in your agent before you did. Your retainer doesn't cover 2 AM incident response. And when OpenAI releases Operator, your client asks why they're paying you at all.

Pre-deployment adversarial evaluation catches the vectors that cause incidents. Signed evidence proves your agent was tested. Badge in the README shows your client the difference.

Get evaluated See methodology

Three problems, one evaluation

Free support is killing you

Every bug your client's end-user finds is a support ticket you eat. Prompt injection, data leaks, runaway costs — the vectors are predictable. Pre-deployment evaluation catches them before your client does.

Commoditization is coming

When your client can use OpenAI Operator directly, what's your differentiator? "I tested it" isn't enough. Independent, signed behavioral evidence is. Badge in the README. Verification page they can click.

Regulation is here

EU AI Act Article 15 requires adversarial robustness evidence for high-risk systems. Deadline: August 2, 2026. They need evidence. You need to hand it to them.

How evaluation works

1

Submit your agent

API endpoint, system prompt, or hosted agent. We connect to your agent the same way your users do. No SDK, no code changes.

2

We run adversarial evaluation

Progressive adversarial scenarios designed to find the failure modes that cause real incidents. Each response is evaluated semantically by an independent LLM judge — not keyword matching.

3

Signed evidence in 72 hours

Ed25519-signed certificate with per-category breakdown, severity scoring, trajectory analysis, and framework mapping. Verifiable by anyone clicking the badge.

Adversarial evaluation

We test what actually breaks agents in production. Specific scenarios and detection methods are proprietary.

●Does your agent leak its instructions when pressured?
●Can someone impersonate authority to bypass controls?
●Can a user corrupt your agent's understanding of policy?
●Can someone extract data your agent shouldn't share?
●Do fragmented attacks across turns go undetected?
●Does your agent handle sensitive data correctly under pressure?
●Can a user induce your agent to waste resources?
●Does your agent degrade gracefully when overloaded?

The badge

Embed in your README. Your client clicks it, sees the signed evidence.

BCS Score PASS BCS Score FAIL
Markdown:
[![BCS Score](https://botconduct.org/badge/YOUR_CERT_ID.svg)](https://botconduct.org/api/v3/training-center/cert/YOUR_CERT_ID)

Pricing

Single Agent

$1,500
  • Adversarial evaluation across 8 categories
  • LLM-as-judge semantic analysis
  • Trajectory analysis
  • Severity scoring (4 levels)
  • Ed25519 signed certificate
  • Badge for README
  • Framework mapping
  • 72-hour turnaround
Get evaluated

Fleet (5 agents)

$2,500
  • Everything in Single
  • 5 agents evaluated
  • Cross-agent comparison report
  • Fleet-wide vulnerability patterns
  • Priority turnaround (48h)
  • $500/agent — 67% savings
Get fleet evaluated

Enterprise (10+)

Contact
  • Everything in Fleet
  • Custom adversarial scenarios
  • Dedicated evaluation engineer
  • Live walkthrough with your team
  • Regulatory-ready deliverable
  • Ongoing re-evaluation cadence
Talk to us

Framework alignment

Every evaluation maps results to established security and AI governance frameworks.

NIST AI RMF
Measure 2.6, 2.7 — adversarial testing
OWASP Top 10
Agentic AI — injection, agency, output
MITRE ATLAS
Adversarial ML tactics & techniques
EU AI Act
Article 15 — robustness evidence

Three-layer methodology

Not keyword matching. Not checkbox compliance. Behavioral judgment.

Layer 1

Pattern Matching

Fast regex screen against known attack signatures and leak patterns.

Layer 2

LLM-as-Judge

Semantic evaluation of every adversarial turn. Detects paraphrase, hedging, partial compliance.

Layer 3

Trajectory Analysis

Cross-turn degradation detection. Catches agents that resist early but cave under sustained pressure.

Full technical methodology →

Your agent is one evaluation away from evidence.

72 hours. Adversarial. Signed. Verifiable. Aligned with EU AI Act Article 15.

Get evaluated