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.

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{Support copilots}{Sales agents}{Ops automations}{Coding agents}{Gemini}{CLI}{MCP}
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$ curl -X POST https://api.latentops.space/v1/runtime/review -H "X-API-Key: $LATENTOPS_API_KEY"
  1. Create an API key from the dashboard
  2. Send an action review request
  3. Log the result in your workspace

Cost-performance at any scale

Estimate runtime-check coverage for your workload.

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I'm protecting aAI operations workspacefor aproduction team

Your AI actions, always visible

Use the dashboard to spot incidents, inspect traces, and tune policies without leaving operators blind.

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

Where teams put LatentOps first

{ai agents}

Controlled tool use for agents

Stop unsafe shell commands, file writes, data exports, and external actions.

toolsactions reviewed
{workflow automation}

Guard high-impact automations

Gate deploys, config edits, migrations, and record mutations.

opsdeploys / changes
{security teams}

Evidence for risky actions

Review what was attempted, why it was blocked, and which policy matched.

auditreview trail
{platform teams}

One path across AI surfaces

Apply the same controls across terminals, copilots, MCP servers, and internal tools.

MCPsurface coverage
Enterprise

Building for your organization?

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

Explore Enterprise

Secure

Scoped keys, roles, policies, and private deployment options.

Compliant

Review history and reports for audits, procurement, and readiness checks.

Reliable

Fast decisions for real agent workflows, not offline-only review queues.