Choose Your Model, Not Your Vendor: Running FlowAgents on Any LLM, Anywhere

How to Adopt AI When Your Data Can’t Leave the Building

For a lot of teams building agentic AI right now, the model is the platform. Pick a provider, send your infrastructure data to their cloud, and everything downstream, every agent, every workflow, is locked to that one choice. That works fine until it doesn’t: a European data residency requirement, a regulated industry review, a compliance team that needs to know exactly where a model runs and what it can see, or simply a better model shipping from a different provider six months from now.

This isn’t a fringe concern. Cisco’s 2026 Data & Privacy Benchmark Study found that outright bans on GenAI tools dropped from 28% to just 7% in a single year, not because the risk disappeared, but because blanket bans don’t work and organizations shifted to governing AI at the point of use instead. A 2026 Cloudian survey of enterprise IT decision-makers found 93% had already repatriated AI workloads from public cloud, were in the process of doing so, or were actively evaluating it, with data sovereignty as the leading driver. Nobody’s banning AI anymore. They’re demanding control over where it runs.

That’s a compliance question about one model on one cloud, not a rule about AI itself. Once the model can run entirely inside your own infrastructure, the answer changes from “we can’t” to “not with that model, but yes with this one.”

Powered by FlowAI, the agentic harness of the Itential Platform, FlowAgents run on any LLM: open weight models like NVIDIA Nemotron, Kimi K2, Llama, or OpenAI’s gpt-oss running on infrastructure you control, proprietary models like Anthropic’s Claude, OpenAI’s ChatGPT, or Google’s Gemini, or a fully custom model your team built and fine-tuned in-house. The model is a choice you make per agent, not a commitment you make for the whole platform. Every agent executes through the same governed path: scripts and automations running through Itential Gateway, direct API calls, or platform workflows, with the same RBAC, approvals, and audit trail underneath, regardless of which model did the reasoning.

Why You Should Attend

If “we can’t do AI here” is the answer your compliance or security team has been giving, or you’re an AI or platform team tired of your agent roadmap being hostage to one provider’s pricing and release schedule, this session is for you. You’ll see exactly what changes that answer, and how to make the case to the people who need convincing.

Why Itential

Most agentic platforms pick a model and build the whole product around it. Itential was built the other way: FlowAI is the harness, the model is a plug-in. Use Itential’s own default out of the box, or bring your own, open weight, proprietary, or built and fine-tuned in-house. That’s not a bolt-on feature, it’s how the platform is architected. No re-platforming when a better model ships. No exception process to run one agent differently than the rest. Just a setting.

Why It Matters Now

Betting an entire agentic platform on one model provider creates two problems at once: a compliance problem, if that provider’s cloud isn’t where your data is allowed to go, and a strategic problem, if a better or cheaper model ships somewhere else next quarter. Choosing per agent instead of per platform means the compliance team gets a model that never leaves your infrastructure where that’s required, and the AI team gets the best available model everywhere else, without anyone re-platforming to get either one.