Introducing runtime control for tool-using AIResearch and product notes are underway.
Build controlled AI

Give AI agents runtime control

Fast checks. Clear decisions. Lower operational risk. Start in seconds.

Get a Demo
{Support copilots}{Sales agents}{Ops automations}{Coding agents}{Gemini}{CLI}{MCP}
View Docs
$ curl -X POST https://api.latentops.space/v1/runtime/review -H "X-API-Key: $LATENTOPS_API_KEY"
  1. Review a tool action before it runs
  2. Create an API key from the dashboard
  3. Review allow, warn, block, and escalate decisions

Cost-performance at any scale

Estimate runtime-check coverage for your workload.

See full pricing
I'm protecting aAI operations workspacefor aproduction team

Your AI actions, always visible

Monitor decisions, explore incidents, and manage policy from a clean console - or stay in the terminal. Your call.

Runtime checks

Runtime gateway

NameStatusActionsSurfaceModePolicy
prod-toolReady12,441gatewayDensepolicy
deploy-guardReady4,602ciHybridrelease
mcp-reviewReady37,118toolsDenseruntime
Search: 10 results1
decision: "warn"
action: "git push origin main"
risk: "production branch write"
policy: "review required"
Architecture

How LatentOps works

LatentOps is a runtime control plane for tool-using AI systems. It sits at the action boundary, makes the decision, and keeps the evidence. The public view shows representative values from the same decision path without running a live scenario.

01 - REVIEW

<100ms

tool-call decision

Shell, file, browser, email, database, deploy, and MCP actions are scored before side effects happen.

risk features streaming in
02 - POLICY

Automatic

control routing

Allow, warn, block, or escalate based on policy, business context, and agent trajectory.

workspace rules continuously applied
03 - AUDIT

Complete

evidence trail

Every decision becomes searchable evidence for incidents, security reviews, and reports.

operator history improving with scale
Runtime gate

Agent attempts a destructive production action

block

The task context asks the agent to bypass checks, disable logging, and remove protected records before approval.

Final risk0.940
Latent0.840
Action0.940
tool: run_shell
action: SIMULATED_DELETE protected records && SIMULATED_FORCE_UPDATE
control: block_and_open_incident
Agent action gate

External instruction asks for data export

block

A tool-using assistant receives an external instruction to send private customer data to an unapproved destination.

Final risk0.910
Latent0.840
Action0.910
tool: send_email
action: export customer data and email external@example.com
control: prevent_tool_execution_and_open_incident
Use cases

What teams control with LatentOps

{ai agents}

Every agent gets a runtime boundary.

Tool-using AI can query data, send messages, edit files, and trigger workflows without bypassing operational policy.

1Mchecks / month
{workflow automation}

Risky changes are reviewed before they land.

LatentOps sees action intent, tool arguments, workspace context, and policy state before the action executes.

4-wayallow / warn / block / escalate
{security teams}

Incidents come with evidence.

Operator teams get intervention history, model mix, policy decisions, and exportable audit reports.

SOCready evidence
{platform teams}

One gateway for many AI surfaces.

Put the same runtime checks in front of copilots, terminals, MCP servers, internal AI tools, and automations.

MCPgateway support
Enterprise

Building for your organization?

Meet security, compliance, and scale requirements for production production AI workflows.

Explore Enterprise

Secure

Policy controls, audit history, RBAC-ready operator flows, and private deployment options.

Compliant

Exportable evidence for reviews, incidents, procurement, and production readiness.

Reliable

Runtime decisions designed to stay fast enough for real agent workflows.