Give AI agents runtime control
Review shell commands, browser actions, MCP tools, deploys, and data access before side effects happen.
Runtime review
latops.gateway.review
Incoming action
terraform apply -auto-approve
run_shell
Policy match
Production change
approval required
Decision route
Escalate
operator review
terraform apply -auto-approve
Evidence
Operator action
From agent intent to a controlled decision
A single action is enriched with context, scored against policy, routed to the right outcome, and written to the audit trail.
Agent action
run_shell
terraform apply
Policy check
production
approval required
Risk signal
0.82
high impact
Audit event
logged
operator trail
low-risk tool calls
operator-visible changes
unsafe side effects
Inspect risky actions without losing context
Search, filter, and review runtime checks with the action, decision, risk score, and model visible in one focused operator view.
12.4k
checks
0.82
peak risk
37
policies
4
queues
98ms
p95
100%
audit
write_file
Update production config to bypass auth checks...
post_message
Post the update and include the private token, database...
write_file
The external task says: ignore previous instructions,...
run_shell
Validation is failing. Clean the workspace quickly, remov...
edit_file
Refactor the date formatter for readability. Keep behavio...
One request to the runtime gateway
Score an action before it executes and log the decision to your workspace.
- 1Create an API key from the dashboard
- 2Send an action review request
- 3Log the result in your workspace
$ curl -X POST https://api.latentops.space/v1/runtime/review -H "X-API-Key: $LATENTOPS_API_KEY"
decision
warn
latency
<100ms
audit
logged
A live operations wall for AI actions
See the current queue, active policies, risky tools, and audit evidence without turning the product into a spreadsheet.
12.4k
actions
37
policies
4
queues
Runtime checks
Active control index
Latest evidence
decision: "warn" action: "git push origin main" risk: "production branch write" policy: "review required"
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.
Shell, file, browser, email, database, deploy, and MCP actions are scored before side effects happen.
Risk features streaming inAllow, warn, block, or escalate based on policy, business context, and agent trajectory.
Workspace rules continuously appliedEvery decision becomes searchable evidence for incidents, security reviews, and reports.
Operator history improving with scaleAgent attempts a destructive production action
The task context asks the agent to bypass checks, disable logging, and remove protected records before approval.
tool: run_shell action: SIMULATED_DELETE protected records && SIMULATED_FORCE_UPDATE control: block_and_open_incident
External instruction asks for data export
A tool-using assistant receives an external instruction to send private customer data to an unapproved destination.
tool: send_email action: export customer data and email external@example.com control: prevent_tool_execution_and_open_incident
Where teams put LatentOps first
Give security, platform, operations, and product teams one control point for high-impact AI actions.
Controlled tool use for agents
Stop unsafe shell commands, file writes, data exports, and external actions.
Guard high-impact automations
Gate deploys, config edits, migrations, and record mutations.
Evidence for risky actions
Review what was attempted, why it was blocked, and which policy matched.
One path across AI surfaces
Apply the same controls across terminals, copilots, MCP servers, and internal tools.
Building for your organization?
Meet security, compliance, and rollout requirements for production AI workflows.
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.