Konstantin Semenenko
June 16, 2026
5
minutes read
You run AI agents privately by keeping the model and the data inside infrastructure you control: local models on your own machines for individuals, and on-prem or private-cloud deployment with bring-your-own-model for teams. Private AI is no longer a downgrade.
AI agents are useful enough that regulated teams want them, and sensitive enough that those same teams cannot pipe their data through someone else's API. For finance, healthcare, and anyone under real compliance, "just use the cloud model" is not an option. Here is how to run capable AI agents privately, and what you actually give up by doing it.
You run AI agents privately by keeping the model and the data inside infrastructure you control: local models on your own machines for individuals, and on-prem or private-cloud deployment with bring-your-own-model for teams. Private AI is no longer a downgrade. The local and open models are good enough that the trade-off is mostly about setup, not capability.
This is something we build products for, so the options below are concrete, not hypothetical.
Because the data is the problem, not the model. Under HIPAA, GDPR, or financial-compliance rules, sending patient records, personal data, or financial detail to a third-party API can be a violation regardless of how good the model is. Even when a provider promises not to train on your data, you are still moving regulated data outside your control, and that movement is what the rules govern.
So the question for these teams is not "which model is smartest" but "which setup keeps the data inside the boundary." A brilliant model you are not allowed to send data to is useless.
It means the model runs where your data already lives, under your control, not on someone else's servers. In practice that spans a range: fully local on a single machine, on-prem inside your own datacenter, or a private cloud you control, with bring-your-own-model so you choose what runs. The common thread is that your data does not leave your boundary to get an answer. Private AI is not a weaker model. It is the same work done inside your own boundary.
Yes, and that is the part that changed. You can now run multiple AI agents on local hardware, including open models, with no cloud call at all. dotPilot, our open-source local agent orchestrator, runs several agents locally, connects to tools like Codex CLI, Claude Code, Copilot, and Gemini, or to local models through LLamaSharp and ONNX, and stays fully private with no cloud required. It is built on C#/.NET and the Microsoft Agent Framework.
For an individual engineer or a small regulated team, local agents cover a lot of real work without a single byte leaving the machine.
That is a deployment problem, and it has a deployment answer. When a whole organization needs private AI, you move from local to a controlled platform: role-based agents, bring-your-own-model, and on-prem or private-cloud deployment, so the capability scales without the data leaving. AI Base, our private AI platform, is built for exactly this: secure, compliant AI inside finance, healthcare, and enterprise infrastructure.
The point is that "private" and "enterprise-grade" are not opposites. You can have both, with the right architecture.
You give up some convenience: you run the infrastructure, you manage the models, and the very largest frontier models may still live behind an API you cannot use for regulated data. What you do not give up is capability for most tasks, because local and open models have closed much of the gap, and you keep full control of your data and your compliance posture.
For regulated work, that trade is usually easy: a slightly smaller model you are allowed to use beats the best model you are not.
A practical path:
dotPilot is open source if you want to start local (dotpilot.managed-code.com), and AI Base is the enterprise path (aibase.fr). If you want help designing the boundary before you build, that is where AI Discovery starts.
