Why this exists
Tool-using AI systems do not just answer questions. They query data, send messages, run shell commands, edit files, call APIs, and touch production systems. That means the risk surface is operational, not only conversational.
We make AI systems observable and controllable before they act.
LatentOps sits at the tool boundary for AI agents and automations. We review proposed shell, file, browser, database, email, deployment, and MCP actions before execution, then route each action to allow, warn, block, or escalate with policy evidence.
Company
Why we are building this
Teams are starting to give AI agents real access to data stores, SaaS tools, terminals, workflows, and production-adjacent systems. The missing layer is not another chat surface. It is a runtime control plane that can see risky actions before they happen and leave an audit trail after they are reviewed.
LatentOps is built for the teams that want agent autonomy without losing operational control. Our product combines pre-tool-call review, policy enforcement, incidents, model analytics, and audit exports in one operator surface.
Thesis
The right product category is runtime safety for tool-using AI.
LatentOps is not just another model analytics dashboard. It is a decision system at the tool boundary.
Why observability is not enough
Post-hoc traces are useful, but they start after the action already ran. For autonomous AI workflows, the higher-value decision is whether the proposed tool call should execute at all.
What LatentOps does
LatentOps acts as a runtime safety layer. It reviews the proposed action with policy, workspace context, action risk, trajectory drift, and optional latent signals, then returns allow, warn, block, or escalate before side effects happen.
Why the dashboard matters
If the gateway is the control point, the dashboard is the operating surface. Security, infra, and engineering leaders need incidents, intervention history, model mix, and exportable evidence from the same decision stream.