Every employee gets their own AI agent.On your infrastructure.
agentctl is a control plane you install on your own servers. It gives each employee a personal AI agent with its own identity, explicit permissions, a full audit trail, and a hard cost cap — governed by your IT, talking to the model provider you choose.
agentctl runs entirely on your infrastructure. We have no access to your data.
All model providers, custom model providers, open-source models.
The stages of AI independence
Where does the value of AI accumulate — at a provider, or at you?
Nothing accumulates. Every conversation starts from zero.
Useful automation, built at the provider. Your prompts and workflows pile up inside someone else's product.
Data, skills, and workflows accumulate at you, in systems you own — and they compound.
What your agents accumulate becomes training data for a model you host. The intelligence itself accumulates at you.
This isn't hypothetical: AI companies are already paying for the operational data businesses generate internally — some offers reaching into the millions — and Gartner projects enterprise AI-data spend to grow roughly 6x between 2024 and 2027.Fast Company
Runs on your infrastructure. Always.
Every agent runs on servers you control and talks to a model provider you chose. agentctl is software you install; we have no access to what your agents read, write, or send.
Sensitive data gets a second wall. PII is redacted before a prompt leaves your infrastructure for the model provider — fail-closed by default: if the filter cannot run, the prompt does not leave. You can add your own entity lists, client names and internal codenames, as a further layer.
How it works
Every call an agent makes passes through agentctl's orchestrator on your infrastructure. It enforces permissions and cost caps, applies the PII filter, and writes the audit log before the call reaches an external model.
Works in mail, files, and internal systems — same as today.
One per employee, on their workstation — own identity, own permissions.
Every call passes through here. Nothing goes around it.
Anthropic, OpenAI, or a model you host — swappable, never a lock-in.
Why your IT department can trust agentctl
Each employee's agent is a dedicated OS user with its own identity, its own permissions, and its own history. Accountability maps one-to-one to people.
Sensitive data is stripped before a prompt reaches the model provider. If the filter cannot run, the prompt is blocked. Your own entity lists — client names, internal codenames — add a further layer.
What an agent can run, read, or send is explicit and auditable. Changes are reviewed and promoted across the fleet, with a git history.
Every action an agent takes is logged. Nothing happens off the record.
Set a ceiling per agent and the orchestrator enforces it. A runaway agent stops at the cap.
Run on Claude, GPT, or a model you host yourself. Switching providers is configuration. agentctl depends on no single model vendor.
See it on your own infrastructure
We'll walk you through the architecture and what it takes to run agentctl on your servers.