Enhance your Agentic AI
A security-first engine for MoE committees and LLM agents: orchestrate attacks, evaluate failure modes, and harden behavior with data-driven guardrails.
ADVERSARIAL • OBSERVABLE • RESILIENT
CAPABILITIES
Advanced security testing and resilience engineering for AI systems through adversarial simulation, committee-based reasoning, and deterministic evaluation frameworks.
Adversarial Scenarios
Script jailbreaks, tool-abuse and data-poisoning flows against single agents or committees.
Committee Orchestration
MoE panels with role diversity, weighted voting and guardrails to minimize single-model blind spots.
Deterministic Replays
Lock versions, seeds and datasets so every failure is reproducible and debuggable.
ThreatOps: Red Team & MoE Intelligence
Attack Scenario Builder
Compose multi-turn attacker/agent dialogues with auto-mutations (role-swap, paraphrase, obfuscation) to stress-test instructions, tools and policies.
Committee Reasoner (MoE)
Diverse experts (reasoners, rule-followers, skeptics) with weighted voting, abstain rules and tie-breakers.
Evaluation Harness
Judge conversations with rubric-guided LLM graders, exact-match checks and policy classifiers; export runbooks to JSON for CI.
Audit Trail & Evidence Graph
Log every token path, tool call and decision with hashes and diffs. Link failures to the exact prompt and model snapshot.