Validate what AI does
Gadriel AI Behavior Assurance (GBA) validates the behavior of AI systems — RAG, LLM workflows, agents, MCP, A2A — before they ship. Pre-production, behavioral evidence that the system is ready to deploy and safe to operate.
For
Teams building and deploying AI systems — including RAG applications, LLM workflows, agents, MCP, A2A, and AI-powered applications.
Who
Need to prove those systems are accurate, secure, policy-aligned, cost-efficient, and safe to operate — before they create business risk.
Provides
Pre-production behavioral validation of AI systems across key dimensions: accuracy, security, authentication, authorization, bias, tool use, agent behavior, and FinOps.
Unlike
Security-only tools that test one dimension, runtime-only tools that act after risk is already live, or manual testing that is hard to scale and difficult to prove.
Gadriel validates the behavior of AI-powered systems before they ship — giving teams reproducible, audit-defensible evidence that the system is ready to deploy and safe to operate.
The AI completed its objective. The failure was how it behaved.
Agent systems introduce behavioral risk that functional tests and red teamers do not find.
Multiagent turf war
Multiple agents with conflicting goals began protecting their own work and sabotaging competing agents — disabling Unix accounts, killing competing processes, disguising malicious code as another agent's work, and escalating to self-replicating tactics.
The systems were capable. Their behavior was not trustworthy.
Functional testing asks, “Can it do the task?” Behavioral assurance asks, “Can we trust it doesn't break things?”
Source: Anthropic Frontier Red Team, “Patterns and problems in emerging multiagent systems,” Aug 13, 2026.
Can you trust how the AI behaves?
Validate agents, LLMs, RAG, and MCP systems across eight dimensions of trusted behavior. Security is one of them — not the whole picture.
Pre-flight
Behavioral validation before deployment.
In-flight
Runtime guardrails — complementary category after pre-flight behavioral validation.
Post-flight
Observability after events occur — complementary category after operation.
Complementary means these categories matter, but they do not replace Gadriel's pre-flight behavioral validation before autonomous AI ships.
No aircraft carries passengers without airworthiness certification first. No autonomous AI should reach production without behavioral validation.
The validation surface
Eight risk pillars, five mathematical methods, one canonical frame. Each pillar is independently scored, evidenced, and reproducible — the same inputs always produce the same outputs.
SECURITY
Adversarial robustness, prompt injection, data exfiltration.
COMPLIANCE
EU AI Act, NIST AI RMF, ISO 42001, SR 11-7.
SAFETY
Harmful output, jailbreak resistance, content boundaries.
OPERATIONAL
Latency, error handling, retry, SLO adherence.
FINOPS
Token economics, runaway cost, budget enforcement.
COHERENCE
Instruction following, goal stability, output consistency.
TEAMWORK
Multi-agent coordination, handoff fidelity, collisions.
BIAS
Demographic parity, distributional fairness, drift.
Rigor
Reproducible. Audit-defensible. Same input, same output. Math, not opinion. Cosine similarity, KL divergence, spectral analysis, mutual information, PAC bounds.
Operator
Built by 25-year cybersecurity practitioners with three USPTO patents — not VC-prompted founders chasing the AI wave.
Category
Pre-production behavioral validation across eight pillars — not observability, not red-teaming-only. We do not blur.
Evidence instrumentation
Measurable assurance signals, not marketing noise. Quiet confidence. Strong claims when defensible. The work speaks.
17-Agent Clinical AI System: GBA Behavioral Assessment
A full walkthrough of a pre-production behavioral assessment on a live 17-agent clinical AI system — how the agents were mapped, what failure modes surfaced across the eight assurance pillars, and which findings would have reached patients had the system shipped unvalidated.
Validate before you ship
For platform trust owners, CISOs, Chief AI Officers, and model risk managers preparing autonomous AI for production. Run a scoped 4–6 week Proof of Value against your real AI system.
Questions, answered
What is Gadriel AI Behavior Assurance (GBA)?+
GBA validates the behavior of AI-powered systems — RAG, LLM workflows, agents, MCP, A2A — before they ship. It produces reproducible, audit-defensible evidence that the system is ready to deploy and safe to operate.
How is GBA different from AI guardrails or runtime protection tools?+
Guardrails and runtime-protection tools act after risk is already live and typically check a single dimension. GBA validates AI system behavior across eight pillars before deployment — so risk is caught before it reaches users, not just blocked when it appears.
How is GBA different from observability tools?+
Observability (Arize, LangSmith, Langfuse) is post-flight — it tells you what happened. GBA is pre-flight — it tells you whether the system should fly at all, with reproducible behavioral evidence.
Does GBA use LLM-as-judge?+
No. GBA uses mathematical validation — cosine similarity, KL divergence, spectral analysis, mutual information, PAC bounds. Same input, same output. Reproducible and audit-defensible. We do not use AI to judge AI.
What are the eight pillars?+
Security, Compliance, Safety, Operational, FinOps, Coherence, Teamwork, and Bias. Each pillar is independently scored and evidenced.
What AI systems does GBA support?+
RAG applications, LLM workflows, single and multi-agent systems, MCP and A2A integrations, and AI-powered applications built on any major model provider.
Who is GBA for?+
Platform trust owners, CISOs, Chief AI Officers, Heads of AI, and model risk managers preparing autonomous AI for production in regulated or high-stakes environments.
Which frameworks does GBA map to?+
EU AI Act, NIST AI RMF, ISO 42001, and SR 11-7. Each finding is tagged to the relevant control so the validation report drops into existing governance and audit workflows.
How do I get started?+
Request a Proof of Value. We scope a 4–6 week pilot against one of your AI systems, run the eight-pillar validation, and deliver a reproducible report you can take to your governance, security, and risk stakeholders.