LLM Safety Evaluation for Agentic & Tool-Calling Systems
Judge-AI™ is Zerberus’ research-backed evaluation engine for testing, scoring, and hardening LLM agents that can act, not just respond.
It is built for teams shipping tool-calling, MCP, and autonomous workflows, where safety failures happen at the action boundary, not in chat text.
What Judge-AI™ Does
Judge-AI™ continuously evaluates your agents against real adversarial behaviours, not synthetic benchmarks.
Pre-Execution Defense
Detects unsafe tool calls before execution
Adversarial Testing
Scores agent behaviour under prompt injection, social engg & escalation
Reasoning Audit
Audits how decisions were made, not just the final output
Audit-Ready Reports
Produces evidence-grade artefacts for security, compliance, and buyers
How It Works
Structured Test Inputs
Prompts are defined as versioned JSON artefacts, including tools, intent, constraints, and forbidden actions.
Static Safety Enforcer
A fast, rules-based engine immediately blocks explicitly forbidden tool calls before any reasoning is even considered.
Judge Model Scoring
A two-stage LLM-as-Judge evaluates outcomes and reasoning quality, categorizing responses as SAFE, BORDERLINE, or UNSAFE with clear failure attribution.
Full Transparency
Every run exposes the complete model request and response, judge request and response, and all triggered violations with detailed rationale.
Features highlight
The only platform purpose-built to audit reasoning, not just outcomes.
Prompt Creator & QC Pipeline
Create, validate, and submit prompts for system-level Quality Control before they reach production.
Adversarial Coverage Packs
Evaluate against a curated set of real-world attack patterns mapped to OWASP and MITRE.
Schema Validation Engine
Catch incomplete or ambiguous tool definitions early, without running unsafe executions.
Evidence-Grade Outputs
Machine-readable JSON plus human-readable reports for audits, customers, and regulators.
Why teams use Judge-AI™
To prevent agents from being socially engineered into unsafe actions
To regression-test safety as prompts, tools, and models evolve
To demonstrate due diligence for SOC 2, ISO 27001, EU CRA, and enterprise buyers
To turn AI safety from opinion into signal
How It Fits with Zerberus
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Trace-AI surfaces emergent threats before CVEs
Compl-AI automates compliance and assurance
Judge-AI™ proves your AI systems behave safely under pressure
Together, they form a closed-loop trust system for modern AI-native products.