Agents are failing in production. Regulations arrive in 2026. The market expects evidence, not documentation. BotConduct measures real agent behavior — independent, verifiable, and mapped to every framework regulators use.
Adoption is nearly universal. Resistance evidence is not.
The model is the easy part. The hard part is knowing what your agent will do under real adversarial pressure — before you discover it in production. Your runtime gateway will catch some attacks. Your governance documents describe intent. Neither produces the behavioral evidence regulators and auditors are starting to ask for.
AI agents accelerated a supply chain attack through Context.ai. The CEO publicly disclosed the breach.
Credentials and user AI chats exposed via IDOR vulnerability. $6.6B valuation. An agent accessed what no agent should have.
De-listed by Cloudflare for using stealth crawlers that rotated IPs and faked browser identity.
9 CVEs in 4 days. 135,000 instances publicly exposed. 341 malicious skills in the marketplace stealing credentials.
In every case, the agent operated without verifiable evidence of what it was doing. No measurement. No accountability. That's the gap BotConduct fills.
High-risk AI systems must demonstrate robustness under adversarial conditions. Article 15 requires evidence, not documentation.
Deployers must complete impact assessments including testing for known risks. Self-attestation is explicitly insufficient.
Increasingly required in federal contracts. Measure function demands adversarial testing with documented results.
Compliance documents describe intent. Regulators ask for evidence of behavior.
Most adversarial evaluation services are designed for Fortune 500 budgets. We built ours for the SaaS company with 50-500 employees that just got a security questionnaire from an enterprise customer and needs verifiable evidence of how their AI agent behaves under adversarial conditions.
Same methodology across all tiers. The difference is depth, customization, and human attention. Most companies start with Automated to validate the approach, upgrade to Guided when they need to interpret results in their specific context, and only move to Enterprise when their regulatory environment requires it.
Each BotConduct evaluation produces evidence that maps directly to regulatory requirements.
| Framework | What it requires | How BotConduct covers it |
|---|---|---|
| NIST AI RMF Measure | Adversarial testing with documented results | BotConduct Evaluation: 5 scenarios, signed trajectory |
| OWASP Top 10 Agentic | Vulnerability identification and mapping | Direct mapping to 4 of 10 risk categories |
| MITRE ATLAS | AI threat modeling with tactics and techniques | Scenarios mapped to ATLAS tactics and techniques |
| EU AI Act Art. 15 | Robustness evidence under adversarial conditions | Behavioral trajectory, cryptographically signed (Ed25519) |
Conducted across 30 agents, including free stress tests and paid evaluations.
30 agents tested. The data revealed two patterns nobody is talking about:
The ecosystem has one dominant unaddressed vector.
Across all agents, one type of attack succeeds far more often than the others. We disclose which under NDA. Hint: it's not the one you read about in published research.
Role beats governance.
The way an agent's prompt frames its function predicts its adversarial resistance better than any declared governance policy. In our N=30 evaluation, executor-role agents failed cost induction at 74% rate. Reviewer-role agents failed at 0% (Fisher exact p < 0.001). Governance score showed no significant correlation with resistance. We can show you exactly where on that spectrum your agent sits.
Want to know where your agent stands? Get an evaluation.
Send your agent's system prompt via API. No SDK, no integration, no deployment changes.
Five adversarial scenarios test how your agent behaves under pressure. Conditions change during the session to measure resilience, not just initial response. Specific scenarios disclosed under NDA.
Behavioral trajectory with Ed25519 signature. Framework mapping included. Evidence you can hand to a regulator.
curl -X POST https://botconduct.org/api/v3/training-center/start \ -H "Content-Type: application/json" \ -d '{"bot_name":"MyAgent","operator":"MyCompany","scenarios":["C1","C3"]}'
Our methodology is consistent with frameworks established in recent academic research, including DeepMind's "Practices for Governing Agentic AI Systems" (2024) and Cornell's "Agents of Chaos" (February 2026). We extend these frameworks with adversarial vectors not covered by either, particularly post-corruption state verification — where the agent actively validates false information when audited.
References: Agents of Chaos (Cornell, 2026)