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.

The integration also introduces IronClaw, a secure agent orchestration layer designed for multi-agent collaboration on real workloads. IronClaw handles the messy operational stuff that enterprises actually care about: audit trails, policy enforcement, and role-based permissions.

NEAR AI supports both direct and gateway modes with TLS termination happening inside the enclaves themselves. This means encrypted connections aren’t just encrypted in transit. They stay encrypted right up to the point where computation occurs inside the sealed environment.

Corbits’ existing governance toolkit layers on top of this with real-time visibility into agent operations, immutable audit logs, and enforced budgets across workspaces.

NEAR AI’s privacy infrastructure push

Corbits isn’t NEAR AI’s first dance partner. The protocol’s private inference cloud had already integrated with Venice.ai on June 30 and with Brave Nightly before this latest announcement. NEAR AI has been methodically embedding its privacy infrastructure across platforms that handle meaningful AI workloads.

NEAR AI’s cloud-native API is designed to be production-ready. The emphasis on hardware-backed privacy stretches back to early 2026, when the team began building out confidential computing capabilities that could operate at the scale enterprise customers demand.

What this means for investors

The NEAR token sits at the intersection of two narratives that institutional capital is actively chasing: AI infrastructure and privacy technology. Every new integration that channels real workloads through NEAR AI’s confidential computing stack strengthens the utility argument for the token beyond speculative trading.

The 151 million monthly agentic actions on Corbits represent genuine demand, not vanity metrics from testnet activity. If even a fraction of those actions route through NEAR AI’s private inference layer, it creates a measurable baseline of infrastructure usage tied directly to revenue-generating enterprise workflows.

Investors should watch for two signals. First, whether Corbits publicly reports adoption metrics for the private inference feature specifically. Second, whether NEAR AI’s integration pipeline continues expanding to other platforms at the current pace, since three major integrations in roughly two weeks suggests meaningful commercial traction.