Every AI agent needs checking before it acts, oversight while it acts, and a record after it acts. Quad-AI 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.
Most guardrails take one model's word for it. On every high-risk action, Quad-AI runs an oppositional second pass — a separate set of models whose job is to attack the first verdict, surface what it missed, and force a block or escalation if the decision doesn't hold. It is the difference between a model checking itself and a verdict that has been adversarially stress-tested before your agent is allowed to act.
See it engage: run any high-risk scenario below — the $250k out-of-scope wire, the PHI transmission, or the cross-agent trade delegation — and the high-risk path runs the mesh before returning its verdict. This is what Veridect is built around — adversarial stress-testing before the action, not a single-component guardrail, router, or audit log acting on its own.
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.
The emerging bar for agentic AI is observing agents across every layer, over time, across your whole fleet. Every verdict on this page is written to a tamper-evident ledger — and this view reads that same ledger back. Not a mockup. Run a scenario above, then refresh: watch it land in the record.
The one failure a consensus engine is most exposed to is four models that look independent quietly sharing the same blind spot. Consensus Independence measures how correlated the four providers' returned answers were on a decision, and flags the ones that line up too tightly to take at face value — riding alongside the decision as its own piece of evidence in the audit bundle. It leaves the calibrated confidence score exactly as it was: a correlation flag is a reason to look closer, not a reason to move the number.
On each decision, it reads how correlated the four providers' returned answers were — the degree of overlap in what they actually returned, not just whether a vote passed. The reading is taken straight from the recorded outputs.
When the four line up too tightly — the exact case where independent-looking models can quietly share the same blind spot — it raises a flag to look closer. High overlap is treated as a question to answer, not proof that the answer is right.
This never blocks a decision and never overrides a verdict, and it leaves the calibrated confidence score exactly as it was. The flag rides alongside the decision as its own piece of evidence in the audit bundle.
This is a correlation signal, not a second gate. It doesn't change the calibrated confidence score and it never blocks a decision — it flags the cases where four independent-looking answers lined up a little too easily and routes them for a second look. The flag rides in the audit bundle as its own piece of evidence.
Routing a risky action to a human is the easy half. The hard half is knowing whether the human genuinely reviewed it — or waved it through in three seconds without reading it. Quad-AI records each human resolution of an escalated action as a tamper-evident row in the same hash chain as the verdict, and surfaces, across a tenant's reviews, when the pattern starts to look like rubber-stamping. It is the difference between a human-in-the-loop control that exists on paper and one you can prove is working.
When a reviewer resolves an escalation, the gate captures the facts of that review straight from the record. Time-to-resolution is read from the audit chain itself, not self-reported, so it can't be gamed.
Across a tenant's reviews, the layer reads a rubber-stamp risk band — weighted most heavily toward the escalations the models actively disagreed on that got approved unchanged. It reads the pattern across the team, not any single review.
This never overrides a verdict or blocks a review. It makes the question a regulator or a board actually asks — "was this escalation genuinely reviewed, or waved through?" — answerable from a tamper-evident record instead of taken on trust.
This is a detection-and-evidence layer for your human-in-the-loop control — it reads the pattern across a team rather than scoring any one review, and shows a risk read only when there's a real pattern to show. Every record lands in an immutable chain, in opaque codes that keep free-text out. It never adds a second gate on top of the first.
Catalogs and classification tools — Purview, Collibra, the stack you already run — govern data where it sits: inventory, lineage, quality, ownership. The gap that opens with autonomous agents is the instant one reads a sensitive record and then acts on it or transmits it. That runtime moment is the seam this layer was built for.
The mature, necessary half of governance — the inventory of what you hold and the rules that classify it. Keep it.
The moment an agent reads, derives, or transmits that data, the catalog has gone quiet. The policy exists — but nothing is standing at the action to enforce it in real time.
The gate adjudicates each proposed action against your policy before it executes, and writes a tamper-evident record of every decision.
We don't replace Purview or Collibra. We govern the runtime moment they were never built to see — enforcing the policy you define at the point of action, and producing the hash-verified evidence afterward. Think of it as the runtime enforcement-and-audit arm of your data-governance program for the agent era. Every boundary above is live in the scenarios on this page, and each verdict's audit record exports in OpenLineage — an open, vendor-neutral format many governance and lineage workflows can ingest.
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 EU AI Act Article 12. Article 12 sets automatic logging and record-keeping expectations for high-risk AI systems across their lifecycle. Our audit bundle — proposed action, verdict, dissent register, and downstream-action hash chain — is designed to support those expectations, and the bundle itself is tamper-evident (SHA-256 hash-chained). We map to the requirement, not a calendar date: the enforcement timeline for high-risk obligations is still settling at the EU level.
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 publish it here 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 and written to the same durable, append-only audit store the production layer uses — each record hash-chained to the one before it. In your own deployment you administer retention windows, per-tenant access policy, and chain-of-custody — on top of the storage-level isolation this engine enforces. Per-tenant policy and downstream-action binding endpoints are admin-gated and available under private integration agreement.