NEAR Protocol has detailed a comprehensive framework for the role of digital assets in an economy dominated by autonomous agents, introducing what it describes as an “AI money” thesis. The protocol is positioning its native infrastructure as the coordination layer for “sovereign AI,” a stack designed to allow AI agents to operate with independent identity, private execution, and native settlement capabilities.

According to official communications from NEAR Protocol, the new economic model for AI agents shifts the function of money into four primary roles. It serves as a store of value to ensure network sustainability, a settlement asset for high-frequency micro-transactions between agents, a bonding standard for security, and a metering unit used to price inference and compute tasks.

Money has to work differently in an agent economy:

✦ Store of value → network sustainability: an asset tied to the security and economic coordination of the network it supports ⁰✦ Medium of exchange → settlement asset: value clearing with finality, cheap enough for…

— NEAR Protocol (@NEARProtocol) July 26, 2026

Infrastructure for Autonomous Agents

The push toward sovereign AI infrastructure focuses on moving beyond open-source models to create a decentralized environment where agents can function without central intermediaries. NEAR has identified several “sovereign agent” requirements that it intends to support through its protocol, including:

• Identity and Verifiability: Procedures to ensure agent authenticity and the ability to verify actions on-chain.
• Private Inference and Confidential Execution: Technologies that allow AI models to process data without exposing sensitive information.
• Open Settlement Rails: Financial infrastructure that allows agents to trade and move value autonomously.
• Liquidity and Coordination: Mechanisms to facilitate interactions between millions of independent digital entities.

The protocol describes itself as a holistic stack that integrates the base blockchain layer with “Intents” and “NEAR AI” to act as a coordinator for this open AI ecosystem. This positioning is detailed in a Q2 2026 report, which frames NEAR’s evolution not as a pivot, but as a return to its founders’ original focus on distributed training and compute systems.

Economic Sustainability and Token Burn

A central component of this infrastructure is the “Intents” layer, which is already impacting the protocol’s tokenomics. Current estimates suggest that the burn mechanism associated with Intents volume is a primary driver of network sustainability. According to some projections, net buyback pressure could exceed total token issuance if daily volume on the Intents layer reaches approximately $175 million, a figure the team views as a milestone for the agent-to-agent economy.

Despite the growth in this sector, the protocol acknowledges significant competition. While NEAR is scaling its infrastructure through milestones such as Multi-Party Computation (MPC) node expansion, its agent framework faces competition from other industry standards. Practical adoption for enterprise AI remains in the proof-of-concept stage, with long-term validation depending on repeatable commercial contracts and the velocity of “post-human” or agent-driven transaction volume.

The success of the “AI money” thesis will likely depend on the protocol’s ability to differentiate its settlement integration as autonomous workloads increase.

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Fetch.ai said it has added two new pieces to its ASI:One ecosystem: an official Skills Library announcement on ASI:One and a Card Playground for building interactive agent cards. The company described the updates as tools for creating, previewing and activating agent-facing experiences more quickly.

In posts on X, Fetch.ai said the ASI:One Card Playground gives users three connected panels for editing card metadata and payloads. The company said the playground supports carousel, detail, form, review and custom card formats, and that users can also describe an interface in plain language or upload a reference image to generate a starting payload with AI.

Many people chat with ASI:One. But they’re only scratching the surface.

(1) Use ‘Projects’ on https://t.co/zIYAcepyTr to folder your activity
(2) Set ‘Recurring Tasks’ so your AI completes tasks while you sleep.
(3) Try ‘Multi-User Chat’ to have you, your friends and all your…

— ASI:One by Fetch.ai (@asi1_official) July 26, 2026

Fetch.ai also said the playground uses the same validation as ASI:One in production, suggesting that cards rendered in the tool should behave the same way for end users. The company framed the feature as a way to “design, test, inspect and connect” interactive cards before writing agent code.

Separately, Fetch.ai announced community-created Skills inside the ASI:One skills library on asi1.ai. According to the post, users can activate abilities with one tap from a right-side panel. The official blog link confirms the Skills Library launch.

The update appears focused on developer and user-interface tooling rather than a broader protocol or token change.

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NEAR Protocol has issued a terminology clarification to better define the scope of its AI infrastructure, distinguishing between the broad concept of an “agent economy” and the narrower function of “agentic payments.”https://twitter.com/NEARProtocol/status/2079235069818945745

The protocol’s core message, shared via official social channels, suggests that the “agent economy” represents a shift in market structure that is significantly larger in scale than the mere transactional layer of automated payments.

According to NEAR, while agentic payments focus on the ability of AI to send and receive value, the agent economy refers to the total orchestration of commercial relationships by autonomous software. The protocol argues that most modern commercial interactions are essentially “transactions waiting for an agent” to manage them.

The clarification identifies three primary pillars that define this emerging category:

• Machine-negotiated services: Autonomous software units determining the terms of service delivery without direct human intervention.
• Agent-to-agent contracting: The creation of digital agreements where AI agents hire and pay other agents to fulfill specific tasks within a value chain.
• Continuous micro-settlement: The high-frequency exchange of value in increments too small or frequent for traditional financial systems to process efficiently.

Infrastructure for AI Agents

The distinction comes as NEAR positions its sharded blockchain architecture, known as Nightshade, as a foundation for “user-owned AI.” By integrating features like the NEAR Agent Market, the protocol aims to provide an environment where AI agents can operate with cryptographic identity and verifiable authority.

Under this framework, blockchain provides the “financial guardrails” necessary for agents to transact safely. This includes establishing reputation systems where agents can build portable transaction histories, which other software counterparts use to evaluate risk or refuse interactions.

NEAR suggests that this model will compress existing value chains by removing human-centric delays. Rather than merely making current payment volumes cheaper, the protocol anticipates that the shift toward machine commerce will lead to a transaction volume that dwarfs existing consumer systems, driven by agents consuming both compute and settlement resources simultaneously.

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