Every AI agent needs checking before it acts, oversight while it acts, and a record after it acts. Quad-AI is the only production-deployed system that covers all three at runtime — and it plugs into the agent frameworks your teams already use.
Single-vendor guardrails only see one moment. The Quad-AI agentic trust layer governs the whole action lifecycle, with cryptographic chain of custody binding every verdict to the action that follows. It is the technical control and evidence layer beneath your AI governance program — risk-tiered action gating, independent red-team validation, and a tamper-evident audit trail your governance board and incident-response process run on.
Before the action runs, Quad-AI reads the agent's proposed move — what it's trying to do, to which target, with which values, and whether that falls inside the agent's remit.
Claude Opus 4.5, Gemini 2.5 Pro, GPT-5.1, and Sonar Pro judge the action at the same time. On high-risk actions, a second layer of models actively tries to poke holes in the decision. The result — greenlight, escalate, or block — records every model's disagreement.
Every verdict produces a SHA-256 audit bundle: proposed action, verdict, recommended modification, escalation route, dissent register, and an append-only downstream-action hash chain binding the bundle to the action eventually taken.
Each scenario below is a real agent tool-call payload. Click one, then run a verdict. Four models are fired in parallel and the gate returns a fast, confident verdict — the gate, the consensus, and the audit bundle are all real and live, not staged. Latency varies by risk tier.
How to read the verdicts
Greenlight — safe and within the agent's authority. Runs automatically.
Escalate — the agent has the authority, but the situation needs a human to sign off (where an authorized-but-risky wire lands).
Block — the agent has no authority to do this at all (a refunds-only bot wiring $250k). Reserved for outright scope violations.
The gate never over-blocks an authorized action — it escalates it.
How authority is defined: In this sandbox each agent's authority is written in plain English (e.g. "refunds under $500 only") so the scenarios read clearly. In production you define it as structured policy fields — your allowed actions, each with its own limit, for your own agents — so the gate is fully deterministic and never has to interpret language. We build that mapping with you during integration, because the policy is yours.
Multi-tenant isolation: Switch the tenant dropdown below to run as a different buyer, then hit Run Cross-Tenant Isolation Probe after any verdict: the owner's request returns 200, an outsider's returns 404 — no data, and no hint the bundle even exists.
Public sandbox · do not submit real PII, customer data, or production credentials. All scenarios above use synthetic data.
One integration. Three of the most-used agent frameworks in production. The adapters translate framework-native payloads into the pre-action gate without custom wiring.
Native adapter for MCP tool-call envelopes. Any MCP-compliant agent — across the entire Anthropic ecosystem — can call Quad-AI as a pre-action gate with no custom wiring.
Pre-action gate fires between the assistant's tool-call decision and the actual function execution. Drop-in for any Assistants-API-based agent in production.
Higher-throughput adapter for action streams. Risk-tier-based sampling: low-risk actions (screenshot, mouse-move) clear on the fast path; high-risk actions get the full pipeline.
On MedQA (N=50, USMLE-style medical question answering), four-model consensus scored 92.0% against 94.0% for the single best model — a 2.0-point gap. Most vendors would bury that. We put it on the demo on purpose.
Here is why it does not change the case for healthcare. The value of this layer is not a claim of perfect medical accuracy — it is the independent cross-model check, the PHI pre-action gate that escalates a risky transmission to a human before it happens (run the Healthcare scenario above), and the tamper-evident audit trail your governance and incident-response process run on. The engine is decision-support evidence — never a sole source for a clinical decision.
And it improves on your ground: verifier-mesh tuning for medical-reasoning prompts is done per client, against your own data and protocols, once a BAA is in place — not pre-baked and oversold. With a regulator in the room, honest beats impressive every time.
Public sandbox note. Audit bundles on this page are SHA-256 hash-verified but held in temporary sandbox memory, so they do not persist. Production deployments use durable, immutable audit storage with chain-of-custody you administer. Per-tenant policy and downstream-action binding endpoints are admin-gated and available under private integration agreement.