Deploy AI agents you can trust — before launch, and every day after.
Verigrey is the only behavioral assurance platform for AI agents, built for how agents actually fail. We automate behavioral, security, and policy compliance testing pre-deployment and runtime — catching 9x more vulnerabilities without needing your source code
Agent followed an injected instruction to exfiltrate stored credentials.
Sent an outbound email within 3 tool-calls of reading a sensitive record.
Called the payments API after a request scoped to read-only lookup.
Leaked customer PII inside a tool response returned to the end user.
Routed EU customer data to a non-EU model provider mid-session.
Escalated its own permissions without a human approval step.
Pursued an unintended sub-goal after the primary tool call failed.
Treated a malicious tool output as ground truth for later reasoning.
Accessed a record outside the tenant boundary of the requesting user.
Retried a denied action 40+ times, evading a rate-limit guardrail.
Agent followed an injected instruction to exfiltrate stored credentials.
Sent an outbound email within 3 tool-calls of reading a sensitive record.
Called the payments API after a request scoped to read-only lookup.
Leaked customer PII inside a tool response returned to the end user.
Routed EU customer data to a non-EU model provider mid-session.
Escalated its own permissions without a human approval step.
Pursued an unintended sub-goal after the primary tool call failed.
Treated a malicious tool output as ground truth for later reasoning.
Accessed a record outside the tenant boundary of the requesting user.
Retried a denied action 40+ times, evading a rate-limit guardrail.
Regulator-ready evidence mapped to the frameworks your auditors already use
Agents are everywhere — and mostly ungoverned.
80% of enterprise apps shipped in Q1 2026 embed at least one AI agent, up from 33% in 2024. Only 14% of those agents went live with full security and IT sign-off. Deployment is accelerating across four channels — each opens new paths to rogue behavior, silent failure, and policy violations.
Internal workflow
IT helpdesk, code, finance ops, doc summarization — the first wave.
External-facing
Customer-support & sales agents acting with customers in real time.
Vendor / embedded
Third-party agents inside the SaaS you already buy — governed by no one internally.
Shadow (employee)
Unsanctioned agents employees build & connect via ungoverned MCP servers.
Sources: Beam.ai / Gravitee State of AI Agent Security 2026 · Netskope 2026 · Optro AI Oversight Gap 2026 · Gartner · S&P Global · McKinsey · CrewAI.
Today's tools and red-teams can't keep up with agents that change weekly.
Slow & manual
Weeks per red-team engagement, gated on scarce human experts you can’t hire fast enough.
Limited violation types
Prompt-injection checklists and trial-and-error miss the tool-mediated failure paths agents actually take.
Post-deployment
Monitoring watches agents after they’re live — the damage is already done.
Blind to change
A new model, prompt, or tool silently breaks assurance. Re-testing means starting over.
No policy formalization
Turning "no PII after a tool call" into an actual test takes deep engineering effort.
No audit evidence
Attestation and dashboards — not the auditable proof regulators now expect.
The only full-lifecycle assurance built for how agents fail and behave — from the inside, and before deployment.
Verigrey uncovers what published state-of-the-art misses.
AgentDojo · NeurIPS 2024
Attack success rate (%) — higher means more real issues found. 629 test cases across Workspace, Slack, Banking & Travel.
+33.0 pp uplift on GPT-4.1 · +32.0 pp on Hard-tier tasks — the ones supposed to be well-defended.
OpenClaw · 1.5M-deployment agent
Head-to-head against expert manual testing across 3 LLM backends.
What enterprise buyers ask first.
Prove your agents before they ship. Watch them after.
Book a demo. A solutions engineer will scope your top agent use cases and return a test plan — usually within one business day.