Phinite For Enterprise
Enterprise AI agents need more than a good model. They need somewhere to run that your security team has already approved, permissions that hold at runtime, and a record of every decision they made. Phinite is the Operating System for Multi-Agent AI.
Trust and compliance
Built to standards your auditors already know
Governance, observability, and access controls that map to the frameworks enterprise risk teams already evaluate against.
Mapped to the NIST AI RMF
Governance in Phinite follows the four functions of the NIST AI Risk Management Framework: govern, map, measure, and manage. Approval gates and version control on one side, execution traces and evaluation on the other.
Security is the barrier, not the ambition
Nearly two-thirds of organizations name security and risk as the top barrier to scaling agentic AI, ahead of regulatory uncertainty and technical limits (McKinsey State of AI). Phinite removes that barrier at the platform layer.
Security
Enterprise security, answered before you ask
The six controls a security review actually tests, and where each one is enforced.
Approaches
What running enterprise AI agents actually requires
Deployment
Cloud-agnostic, private cloud included
Deploy Phinite to AWS, Azure, GCP, or your own private cloud, with data residency options. Nothing ties you to one provider or one model vendor, and no data has to leave a boundary you control to make an agent work.
Integration
Integrations to power your AI agents
Phinite integrates with the tools you already use so your AI agents can take real action, not just respond.
Scale
The problem starts at agent number eleven
The first ten agents are a project. The next hundred are an estate, and an estate needs one control plane rather than ten.
One procurement conversation instead of one per team.
Every agent visible in a single AI agent registry, including the ones you did not commission
Policy set once, applied to agents that do not exist yet
The same permissions, audit trail, and evaluation framework whether you run three agents or three hundred
Rollout
How an enterprise deployment actually runs
Three steps, in this order, because the second one is what the review will ask about.
One Workflow
Not a department. One workflow, designed in Agent Graph Studio so the diagram you drew is the thing that runs.
Policy First
Set the policy, the roles, and the approval gates on that one workflow in Governance, so the pattern is already in place when the second team arrives.
Inherit the Pattern
The next workflow inherits the policy set rather than negotiating its own.
FAQs
Questions procurement and security ask
The ones that arrive after the demo, about deployment, access, audit, and cost.
What are enterprise AI agents?
Can we deploy Phinite in our own cloud?
How is access to agents controlled?
What is retained in the audit trail?
Can we cap what an agent is allowed to spend?
Ready to deploy enterprise AI agents at scale?
Bring us your security questionnaire. We would rather answer it now than after the pilot, in a walkthrough covering governance, environments, identity, and private-cloud deployment.