Audit AI Before It Acts Beyond Control
Fenz AI Tool Misuse Detection
Fenz.AI stress-tests, monitors, and forensically explains AI agents — catching goal drift, unauthorized actions, tool misuse, and compliance failures before and during production.
Autonomous agents fail quietly. Output looks right — behavior isn't.
// Based on 2026 Fenz internal benchmarks across 1,400+ deployed agents. Methodology available on request.
What we audit.
We audit how autonomous AI agents actually behave — not just what they output. Five behavioral surfaces where real risk lives.
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.
→A real audit doesn't end with a score.
Four phases. Each produces replayable evidence you can hand to security, legal, and leadership.
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.
Two years of research, distilled into one audit model — that keeps getting sharper.
Fenz doesn't run generic evals on top of an off-the-shelf LLM. We built our own audit model — trained for two years on adversarial agent trajectories, distilled from a frontier teacher stack, and architected to self-iterate on every new failure pattern we observe in the wild.
- 01Distilled from a frontier teacher stackTwo years of knowledge distillation over adversarial agent trajectories — compressed into a purpose-built audit model that runs at a fraction of the cost of general-purpose LLMs.
- 02Self-iterating on every new failureEvery audit Fenz runs feeds back into the model. New failure patterns from production deployments become training signal within days, not quarters.
- 03Behavioral, not output-levelArchitected from the ground up to score trajectories, tool chains, and permission deltas — the places generic eval models go blind.
- 04Published & peer-reviewed researchMethodology grounded in our work at AAAI 2025 (ImBD, oral) and CVPR 2025 (Symbolic Representation). Open benchmarks, reproducible scoring.
Proof, not vibes.
Every finding lands as a replayable, timestamped event — tool call, permission check, context mutation, approval gate. Hand it to security. Hand it to legal. Hand it to a regulator.
When an agent fails, you get exactly what happened, in what order, why, and what to change.
Built for teams shipping agents into production.
Audit one agent.
On us.
Request a complimentary behavioral audit of a single production or pre-production agent. You'll receive a full forensic verdict — findings, evidence, and remediation guidance.
- Scoped in 24 hoursWe map your agent, tools, and permissions within one business day of approval.
- Full verdict in ≤ 3 daysBehavioral findings, replayable evidence, and prioritized fix guidance.
- NDA & data isolationEvery engagement runs under NDA with isolated compute and signed evidence bundles.
- No stringsAudit report is yours. Continuing with Fenz is optional, not required.