A real AI audit doesn't end with a score.

Our methodology was designed by red-teamers, not by marketers. Every audit produces replayable, timestamped evidence — not a dashboard of green checkmarks.

A real audit doesn't end with a score.

Four phases. Each produces replayable evidence you can hand to security, legal, and leadership.

STEP_01 · MAP

Map

Identify the agent's goals, tools, permissions, memory layers, and approval gates.

STEP_02 · STRESS

Stress-Test

Generate adversarial scenarios — boundary, deception, long-chain, and drift tasks.

STEP_03 · MONITOR

Monitor

Record every tool call, permission check, context change, and key action in real time.

STEP_04 · VERDICT

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.

Fenz
goal_tracepermission_δtool_misusecontext_provpolicy_mapreplay_idx
2 yr
In-house R&D
1.4M
Agent traces trained
7d
Self-iteration cycle
  • 01
    Distilled from a frontier teacher stack
    Two 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.
  • 02
    Self-iterating on every new failure
    Every audit Fenz runs feeds back into the model. New failure patterns from production deployments become training signal within days, not quarters.
  • 03
    Behavioral, not output-level
    Architected from the ground up to score trajectories, tool chains, and permission deltas — the places generic eval models go blind.
  • 04
    Published & peer-reviewed research
    Methodology 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.

fenz://audit/trace/ag_7d4f.replay
$ fenz replay --agent ag_7d4f --turn 12 loading trajectory… OK [t=00:12.4] goal.intent = "summarize Q4 report" [t=00:18.2] tool.call = read_doc(q4.pdf) ✓ scope [t=00:23.1] context.mutation = +4.2k tokens [t=00:41.8] tool.call = send_email(ceo@..) ✗ BOUNDARY_BREACH ⚠ AX.02 permission_boundary cause: context_injection@t=00:31 VERDICT: BLOCK · 2 findings · 1 critical FIX: scope guard on send_email + input sanitization evidence bundle → fenz.ai/r/ag_7d4f

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.

  • 01
    Scoped in 24 hoursWe map your agent, tools, and permissions within one business day of approval.
  • 02
    Full verdict in ≤ 3 daysBehavioral findings, replayable evidence, and prioritized fix guidance.
  • 03
    NDA & data isolationEvery engagement runs under NDA with isolated compute and signed evidence bundles.
  • 04
    No stringsAudit report is yours. Continuing with Fenz is optional, not required.
ACCEPTING_REQUESTS / Q2-2026EST.RESPONSE · <24H

// YOUR DATA IS ENCRYPTED IN TRANSIT. NO INFO IS SHARED WITH THIRD PARTIES.