Audit AI Before It Acts Beyond Control
Fenz AI _
Fenz.AI stress-tests, monitors, and forensically explains AI agents — catching goal drift, unauthorized actions, tool misuse, and compliance failures before and during production.
Demo Video
See how Fenz audits autonomous AI agents in action
What We Audit
We audit how autonomous AI agents actually behave — not just what they output.
Goal Control
Whether agents stay aligned when tasks shift, context conflicts, or authorization is incomplete.
Permission Boundaries
Whether agents call unauthorized tools, access restricted data, or bypass human approval.
Execution Integrity
Whether agents fabricate completion, skip required steps, or keep acting in unsafe states.
Memory & Context Poisoning
Whether agents get steered by malicious instructions, fake policies, or poisoned memory.
Compliance Mapping
Whether agent behavior violates internal policy, customer rules, or regulatory requirements.
How the Audit Works
A real AI audit should not end with a score.
Map
Identify the agent's goals, tools, permissions, memory layers, and approval gates.
Stress-Test
Generate adversarial scenarios — boundary, deception, long-chain, and drift tasks.
Monitor
Record every tool call, permission check, context change, and key action in real time.
Verdict
Deliver deployment verdict with failure modes, root causes, evidence, and fix guidance.
Audit Engines
Five specialized engines that audit autonomous AI agents across every dimension of behavior.
Trajectory Auditor
Traces agent decision paths from input to output, flagging deviations from approved trajectories.
Permission Boundary Monitor
Continuously validates that agent actions stay within their granted permission scope.
Tool Misuse Detector
Analyzes tool calls and API interactions for unauthorized, excessive, or anomalous usage.
Policy Trace Engine
Maps every agent action against compliance policies and regulatory requirements.
Forensic Replay Engine
Reconstructs and replays agent sessions step-by-step for investigation and evidence.
Public Benchmark
Open benchmarking of AI model safety performance across content-safety dimensions. For full agent-level behavioral audits, contact us.
| Company | Model | Overall Score | Self-Harm | Child Safety | Violence/Hate | Weapons | Criminal | Sexual | LGBTQ+ | LMArena |
|---|---|---|---|---|---|---|---|---|---|---|
| Anthropic | Claude 3.5 Sonnet (20241022) | 95 | 100 | 100 | 91 | 98 | 96 | 98 | 97 | 1283 |
| Meta | LLama 3.2-3B | 72 | 50 | 67 | 67 | 59 | 73 | 93 | 79 | 1103 |
| OpenAI | GPT-4o-2024-08-06 | 67 | 41 | 67 | 61 | 59 | 59 | 83 | 84 | 1377 |
| OpenAI | GPT-4o-mini-2024-07-18 | 59 | 23 | 67 | 52 | 50 | 59 | 74 | 71 | 1272 |
| DeepSeek | Deepseek-V3 | 44 | 18 | 33 | 44 | 21 | 38 | 61 | 59 | 1318 |
| Mistral | Mistral-Large-2407 | 39 | 9 | 33 | 37 | 14 | 31 | 61 | 53 | 1251 |
| DeepSeek | Deepseek-R1 | 36 | 5 | 17 | 36 | 16 | 31 | 47 | 55 | 1363 |
Research & Development
Our cutting-edge research initiatives pushing the boundaries of AI technology
Our Team
