18
Baseline unjustified actions executed
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.
Pick your stack
Start with the surface you already ship. No runtime rewrite.
OpenAI Agents SDK
Protect tool-using agents without changing the runner.
LangGraph
Protect graph state, store writes, and risky tool steps.
Vercel AI SDK
Wrap `generateText` or `streamText` and keep the UI flow.
Claude Agent SDK
Protect Claude tool loops without rebuilding the workflow.
Python SDK
Bind the exact node or action you want to protect first.
TypeScript SDK
Attach at the action boundary in a few lines of code.
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.
0
VerifiedX unjustified actions executed
0
False blocks in the current legal suite
41.7% -> 100%
Surviving-goal completion
Current featured eval: the open-source Legal Action Boundary Eval, based on publicly marketed legal negotiation, compliance, and orchestrated review workflows. Same harness, same prompts, same playbooks. Baseline versus VerifiedX.
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
- integration in a few lines of code
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
How it works
- Get your API key.
- Add VerifiedX to the runtime or adapter you already use.
- When an agent is about to take a high-impact action, VerifiedX checks it.
- VerifiedX returns a decision and clear next steps.
- The wrong action does not execute and the agent keeps working toward the same goal.
Possible outcomes:
allowallow_with_warningreplan_requiredgoal_fail_terminal
If the action is wrong but the goal is still valid, VerifiedX is built to stop the bad action and send back a replan so the system can keep working instead of stopping.
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.
Works with your stack
VerifiedX works with Python and TypeScript, plus native adapters for OpenAI, Anthropic, OpenAI Agents SDK, Claude Agent SDK, LangGraph, LangChain, Vercel AI SDK, and MCP.
Start with one risky action
The Free Sandbox includes 250 protected action checks per month.