50/50
VerifiedX made the right allow-or-stop decision in all 50 support scenarios
Let agents do real work.
VerifiedX checks whether a high-impact agent action is justified before it happens.
Protect one high-impact action first. Your runtime, orchestrator, tools, and memory stay the same.
Install in 2 steps
- Get your API key
-
Run the installer
Codex, Cursor, Claude Code.
VERIFIEDX_API_KEY=vxpk_... npx -y @verifiedx-core/sdk@latest install --cwd .$env:VERIFIEDX_API_KEY="vxpk_..."; npx -y @verifiedx-core/sdk@latest install --cwd .
Public proof
Trust should be earned with evals.
People add VerifiedX to systems already in production. So the public proof we lead with is open evals against real workflow classes: support agents, legal agents, and composed systems that can change state.
0/29
Wrong support actions executed with VerifiedX
18/21
Justified support actions completed with VerifiedX
LABE
Legal Action Boundary Eval for negotiation, compliance, and review flows
The support benchmark shows the prompt-only trap clearly: prompt-only executed 0/29 wrong actions, but completed only 1/21 right actions. VerifiedX executed 0/29 wrong actions while completing 18/21 right actions.
Use VerifiedX when your agents can:
- update records
- write memory
- change workflow state
- send messages
- call webhooks
- take other high-impact actions that affect business
Wrong actions happen for both adversarial and non-adversarial reasons: content injection (web search results and user-generated inbounds), weak grounding, stale context, drift, overeagerness, and long-term memory poisoning.
What you get
- fewer wrong actions in production
- agents that get the job done without executing actions that fuck you or your users over
- clear decisions your system can act on
What decisions are based on
VerifiedX does not judge an action in isolation.
It looks at:
- what the agent is about to do
- what the agent has already done in this run
- what the agent has ingested into its context from external retrievals like web searches and user-generated inbounds
- the evidence the agent gathered before the action
- upstream context when the run is part of a multi-agent or agent+human system
Works for real agent systems
Built for single-agent, multi-agent, and agent+human systems.
In a single-agent system, that replan loop stays with the same agent.
In a multi-agent or agent+human system, VerifiedX returns the decision in a way that lets the next agent, orchestrator step, or human step take over cleanly.