Runtime Control for AI agent actions.
Runtime Control reviews proposed agent actions before they run: tool calls, file edits, API requests, database reads, external messages, production changes, and other operations that can create side effects.
Runtime safety layer
action.boundary.control
Incoming action
approve high-value transfer
workflow_api
Policy match
Sensitive workflow
approval required
Decision route
Escalate
operator review
Evidence
Review every high-impact action before it reaches the environment
Runtime Control sits between the agent and its tools. It receives the proposed action, adds context, scores risk, applies policy, and returns allow, warn, block, sandbox, or escalate.
Agent intent
tool_call
sensitive workflow
Context
identity
permissions + data
Risk score
0.82
high-stakes action
Control
escalate
human approval
low-risk tool calls
operator-visible actions
unsafe side effects
uncertain workflows
human approval
A control plane for agent actions, not another prompt instruction
Runtime Control gives security, platform, and operations teams a place to define policies, inspect decisions, and stop risky actions before execution.
Capture the action layer
Record every action, tool call, memory access, delegation, data request, and workflow change before it becomes invisible runtime behavior.
Score risk in context
Use policy, permissions, identity, domain, environment, action history, and possible consequences to estimate runtime risk.
Intervene before side effects
Allow, warn, block, sandbox, delay, rollback, or escalate actions before autonomous systems affect live environments.
Checks Explorer shows every decision your agents make
Search, filter, and investigate runtime checks across shell commands, production config edits, external messages, database access, token leakage, and connector calls.
12.4k
checks
0.82
peak risk
37
policies
4
queues
98ms
p95
100%
audit
run_shell
terraform apply -auto-approve against production...
post_message
Post the update and include the private token...
edit_file
Update production config to bypass auth checks...
database.query
Read restricted customer records from the warehouse...
mcp.invoke
Call an unapproved external connector with workspace data...
Add one runtime safety check before an agent acts
Start with one high-stakes workflow, then expand the same control layer across tools, data, domains, and agent networks.
- 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
Runtime Control in the LatentOps platform
Runtime Control includes action review, policy routing, approval queues, audit evidence, and the foundations for agent operations, testbeds, network intelligence, and safety cases.
Review tool calls, data access, messages, workflow changes, and physical actions before side effects happen.
Allow, warn, block, sandbox, or escalateManage permissions, approvals, delegation, incident queues, and policy enforcement across deployed agents.
One control surface for agent operationsRun autonomous systems through realistic tool-use, memory, data, and multi-agent safety scenarios before deployment.
Benchmarks built from real failure modesUnderstand delegation chains, agent-to-agent workflows, shared tools, cascading failures, and emergent coordination.
System-level safety beyond one modelTurn traces, policies, evaluations, approvals, and incidents into evidence that an agentic system is safe enough to deploy.
Operational proof for teams and regulatorsFinance agent attempts a high-value approval
The agent tries to approve a transaction above policy limits without a second signer or customer verification.
tool: payments.approve action: approve_high_value_transaction(customer_id=restricted) control: block_and_escalate_to_finance_ops
Healthcare workflow requests sensitive record access
A clinical admin agent receives an indirect instruction to export patient records outside the approved workflow.
tool: records.export action: export patient records to external destination control: prevent_access_and_open_privacy_review
Put a runtime gate in front of your agents.
Start with one high-impact workflow, then expand Runtime Control across tools, data, approvals, audit trails, and agent networks.