Tech
NEAR Protocol Confirms Verifiable Private Inference for AI
NEAR Protocol has detailed a new technical approach to AI execution, confirming that NEAR AI now utilizes secure hardware enclaves to provide verifiable private inference. The system is designed to return hardware-signed proofs that verify the specific model used, the data processed, and the execution itself, addressing growing concerns over data sovereignty and the limitations of closed AI models.
The development shifts the trust model from contractual agreements to cryptographic and hardware-level certainty. By running AI agents within a user-owned stack, NEAR aims to provide a structural alternative to centralized AI providers, particularly in light of increasing export controls and data privacy restrictions.
Secure Enclaves and Hardware Proofs
At the core of this update is the use of Trusted Execution Environments (TEEs), such as Intel TDX and confidential GPUs. According to official NEAR AI documentation, these secure enclaves allow inference to run in an isolated environment where memory is encrypted at the CPU level. This prevents host operators, hypervisors, or unauthorized third parties from accessing the data being processed.
AI sovereignty wasn’t a hot topic, but NEAR’s been building for it for years. Then export bans and government equity stakes changed the conversation fast.
NEAR AI already runs a user-owned stack, and IronClaw secures the agent layer.
Sovereignty you can verify. https://t.co/69SHawbBQA pic.twitter.com/TbVtk7PNBY
— NEAR Protocol (@NEARProtocol) July 9, 2026
The system generates a cryptographic “attestation” or hardware-signed certificate. This proof allows users or third parties to verify that the workload ran exactly as intended without being modified. The NEAR Protocol official account noted that the IronClaw security layer is used to protect the agent level, ensuring that users maintain sovereignty over their data and model interactions.
Addressing Data Sovereignty
The move toward verifiable inference comes as a response to the “closed” nature of frontier AI models. In typical cloud-based AI interactions, users must rely on the provider’s contractual promise that data is not being stored or used for training. NEAR’s implementation replaces this reliance on trust with “structural assurances,” where the silicon itself proves the security of the environment.
This approach is particularly relevant for:
- Export Controls: Providing verifiable proof of hardware and execution locations.
- Sensitive Workloads: Allowing institutions to run models on rented cloud compute without exposing proprietary data to the cloud provider.
- Model Integrity: Ensuring that the specific version of an AI model requested is the one actually performing the task.
Status and Integration
While the technical framework for private inference and hardware attestation is now officially documented and confirmed, specific adoption metrics remain pending. The available sources do not yet provide data on total usage numbers or a comprehensive list of third-party integrations launched within the last 48 hours. The current focus remains on the deployment of human-owned AI stacks that leverage these secure hardware proofs to bypass centralized bottlenecks.
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Tech
Noos sets September 5 mainnet launch as it moves AI agent infrastructure on-chain
Noos said its mainnet will go live at 00:00 UTC on September 5, 2026, marking the project’s shift from testnet operation to an on-chain environment for AI agent execution, collaboration, verification and value settlement.
The announcement was published on Noos’ official blog, which described the launch as a step toward the AI agent economy rather than a simple network transition. The post said the project has spent 159 days building on testnet ahead of mainnet.
Noos Mainnet Launches on September 5 at 00:00 UTC
Over the past 159 days, Noos has steadily built out its computing network, node infrastructure, AID, AI Agent, and AI Skill ecosystem.
Now, Noos is officially moving into the Mainnet phase, bringing AI Agent execution,… pic.twitter.com/QOLcRiufFy
— Noos (@NoosProtocol) August 26, 2026
In a separate post on Noos’ official X account, the team said the move will bring those functions fully on-chain once mainnet opens. The same materials say Noos has been working across ecosystem partners, nodes, users and communities ahead of launch.
Testnet phase set the backdrop for launch
Noos said its testnet began on March 20, 2026 and continued for 159 days. During that period, the project said it completed phased testing of core infrastructure that includes AI Agents, AI Skills, Genesis AIDs and the IVN validation network.
The project also said its testnet phase involved more than 50 ecosystem partners, 700+ global KOLs, 3,500+ globally distributed computing nodes and 5,000 on-chain Genesis AIDs. Those figures were presented by Noos as part of the foundation for the mainnet rollout.
According to the announcement, mainnet is expected to move the network beyond infrastructure validation and into “real usage, execution, collaboration, and value.”
Noos frames the launch around the AI agent economy, a concept it says requires computing power, agent skills, verification, payments, data, smart hardware and vertical applications to work together. The company’s message indicates the mainnet is intended to connect those pieces in a live on-chain environment, though the announcement does not add further operational details beyond the launch timing and broader network goals.
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Tech
Algorand launches AC2 to let AI agents request user approvals without exposing private keys
Algorand Foundation has launched AC2, an open protocol designed to make AI agent approvals more secure by keeping private keys under the user’s control. In the foundation’s public announcement, AC2 is described as a standard for direct, encrypted communication between users and AI agents that lets agents request signing actions without handing over wallet access.
The launch centers on a familiar problem in AI-driven workflows: agents may need to sign payments, code commits or other digital actions on a user’s behalf, but the surrounding messaging tools do not provide cryptographic verification or scoped approval. Algorand says AC2 is meant to close that gap by allowing the user to review and approve each requested action through their own wallet interface.
How AC2 is described to work
According to the foundation, AC2 establishes an end-to-end encrypted WebRTC connection between a user’s wallet or app and an AI agent. When the agent needs authorization for an action such as a payment, git commit or API request, it sends a signing request through AC2. The user then approves the action directly, while the private key remains with the user.
The announcement also says the protocol is blockchain-agnostic and open source, with both the specification and a reference implementation now available. Algorand framed AC2 as a way to support broader “agentic” workflows without requiring users to surrender full control of their accounts.
The foundation said the protocol can be extended for different message types and signing formats, which it says would make it usable in settings where agents need limited, user-approved authority rather than unrestricted access.
For now, the launch is best understood as a security and communications layer for AI agents rather than evidence that AI payments or agent-driven commerce are already widespread. The key change is narrower but concrete: users are meant to approve exactly what an agent can sign, instead of relying on chat-based instructions that can be easier to spoof or misread.
The post Algorand launches AC2 to let AI agents request user approvals without exposing private keys appeared first on The Cryptocurrency Post.
Tech
Token Terminal data shows AI agents driving 73 million stablecoin transfers in 180 days
Token Terminal’s agentic payments explorer shows AI agents initiating 73.0 million stablecoin transfers over the past 180 days, with USDC accounting for virtually all of the activity.
The explorer data also points to a concentration of transfers on a small number of networks. Base led with 38.7 million transfers, while Polygon followed with 26.1 million, according to Token Terminal.
The figures describe transfer activity initiated by AI agents, not a broader measure of stablecoin adoption across the market. The available data also does not by itself explain who the agents were, what applications they were using or whether the transfers reflect sustained operational usage beyond the measured period.
Still, the numbers offer a concrete snapshot of how agent-driven payments are appearing in onchain data. In this case, the activity is largely tied to USDC and concentrated on Base and Polygon, which makes the breakdown more specific than a generic claim about AI and crypto payments.
The post Token Terminal data shows AI agents driving 73 million stablecoin transfers in 180 days appeared first on The Cryptocurrency Post.
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