Running AI agents at scale is one thing. Running them without anyone, including the platform itself, being able to peek at your data is another challenge entirely. Corbits, an AI operations and governance platform, announced its integration with NEAR AI’s private inference infrastructure on July 15, bringing hardware-enforced confidentiality to enterprise team workspaces.
The pitch is straightforward: organizations can now orchestrate complex AI agent workflows on Corbits while keeping both their data and the underlying models encrypted and isolated inside Trusted Execution Environments. Think of TEEs as sealed rooms where computations happen. Nobody gets to look through the windows, not even the landlord.
What the integration actually does
Corbits isn’t a small operation. The platform processes over 151 million monthly agentic actions across more than 115,000 agents. That’s a lot of AI workflows humming along, and until now, those workflows relied on conventional inference pipelines where data confidentiality was more of a policy promise than a technical guarantee.
NEAR AI changes the equation by routing inference through Trusted Execution Environments like Intel TDX and NVIDIA Confidential Computing. The hardware itself enforces privacy, not just software rules that could theoretically be bypassed. Every execution produces cryptographic attestations, meaning organizations can mathematically verify that their data was processed in a secure enclave without tampering.
